Publications by authors named "Sonal Gandhi"

53 Publications

Immune Checkpoint Inhibitor-Associated Myocarditis With Persistent Troponin Elevation Despite Abatacept and Prolonged Immunosuppression.

JACC CardioOncol 2020 Dec 15;2(5):800-804. Epub 2020 Dec 15.

Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jaccao.2020.10.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352272PMC
December 2020

Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning.

Transl Oncol 2021 Oct 19;14(10):101183. Epub 2021 Jul 19.

Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada. Electronic address:

Although neoadjuvant chemotherapy (NAC) is a crucial component of treatment for locally advanced breast cancer (LABC), only about 70% of patients respond to it. Effective adjustment of NAC for individual patients can significantly improve survival rates of those resistant to standard regimens. Thus, the early prediction of NAC outcome is of great importance in facilitating a personalized paradigm for breast cancer therapeutics. In this study, quantitative computed tomography (qCT) parametric imaging in conjunction with machine learning techniques were investigated to predict LABC tumor response to NAC. Textural and second derivative textural (SDT) features of CT images of 72 patients diagnosed with LABC were analysed before the initiation of NAC to quantify intra-tumor heterogeneity. These quantitative features were processed through a correlation-based feature reduction followed by a sequential feature selection with a bootstrap 0.632+ area under the receiver operating characteristic (ROC) curve (AUC) criterion. The best feature subset consisted of a combination of one textural and three SDT features. Using these features, an AdaBoost decision tree could predict the patient response with a cross-validated AUC accuracy, sensitivity and specificity of 0.88, 85%, 88% and 75%, respectively. This study demonstrates, for the first time, that a combination of textural and SDT features of CT images can be used to predict breast cancer response NAC prior to the start of treatment which can potentially facilitate early therapy adjustments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.tranon.2021.101183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319580PMC
October 2021

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Oncotarget 2021 Jul 6;12(14):1354-1365. Epub 2021 Jul 6.

Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.

Background: Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer.

Materials And Methods: MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance.

Results: 7 features were significantly ( < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively.

Conclusions: Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.18632/oncotarget.28002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274727PMC
July 2021

Lessons from spending on megestrol for cancer cachexia.

Support Care Cancer 2021 Oct 24;29(10):5553-5555. Epub 2021 Apr 24.

Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00520-021-06240-7DOI Listing
October 2021

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks.

Sci Rep 2021 Apr 13;11(1):8025. Epub 2021 Apr 13.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.

Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-021-87496-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044238PMC
April 2021

prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods.

Oncotarget 2021 Jan 19;12(2):81-94. Epub 2021 Jan 19.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.

Purpose: We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation.

Materials And Methods: QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.

Results: A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy.

Conclusions: A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.18632/oncotarget.27867DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825636PMC
January 2021

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

JCO Clin Cancer Inform 2021 01;5:66-80

Temerty Centre for AI Research and Education in Medicine, University of Toronto, ON, Toronto, Canada.

Purpose: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response (pCR) has prognostic value depending on BC subtype. Rates of pCR, however, can be variable. Predictive modeling is desirable to help identify patients early who may have suboptimal NAC response. Here, we test and compare the predictive performances of machine learning (ML) prediction models to a standard statistical model, using clinical and pathological data.

Methods: Clinical and pathological variables were collected in 431 patients, including tumor size, patient demographics, histological characteristics, molecular status, and staging information. A standard multivariable logistic regression (MLR) was developed and compared with five ML models: k-nearest neighbor classifier, random forest (RF) classifier, naive Bayes algorithm, support vector machine, and multilayer perceptron model. Model performances were measured using a receiver operating characteristic (ROC) analysis and statistically compared.

Results: MLR predictors of NAC response included: estrogen receptor (ER) status, human epidermal growth factor-2 (HER2) status, tumor size, and Nottingham grade. The strongest MLR predictors of pCR included HER2+ versus HER2- BC (odds ratio [OR], 0.13; 95% CI, 0.07 to 0.23; < .001) and Nottingham grade G3 versus G1-2 (G1-2: OR, 0.36; 95% CI, 0.20 to 0.65; < .001). The area under the curve (AUC) for the MLR was AUC = 0.64. Among the various ML models, an RF classifier performed best, with an AUC = 0.88, sensitivity of 70.7%, and specificity of 84.6%, and included the following variables: menopausal status, ER status, HER2 status, Nottingham grade, tumor size, nodal status, and presence of inflammatory BC.

Conclusion: Modeling performances varied between standard versus ML classification methods. RF ML classifiers demonstrated the best predictive performance among all models.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1200/CCI.20.00078DOI Listing
January 2021

Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer.

Oncotarget 2020 Oct 20;11(42):3782-3792. Epub 2020 Oct 20.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.

Background: To investigate quantitative ultrasound (QUS) based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).

Materials And Methods: 100 Patients with LABC were scanned before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric-image, 4 grey level co-occurrence matrix-based texture images were derived (20 QUS-Tex), which were further processed to create texture derivatives (80 QUS-Tex-Tex). Patients were classified into responders and non-responders based on clinical/pathological responses to treatment. Three machine learning algorithms based on linear discriminant (FLD), -nearest-neighbors (KNN), and support vector machine (SVM) were used for developing radiomic models of response prediction.

Results: A KNN-model provided the best results with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 81%, 82%, and 0.86, respectively. The most helpful features in separating the two response groups were QUS-Tex-Tex features. The 5-year recurrence-free survival (RFS) calculated for KNN predicted responders and non-responders using QUS-Tex-Tex model were comparable to RFS for the actual response groups.

Conclusions: We report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.18632/oncotarget.27742DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584238PMC
October 2020

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence.

Can Assoc Radiol J 2021 Feb 31;72(1):98-108. Epub 2020 Aug 31.

Division of Breast Imaging, 71545Sunnybrook Health Sciences Centre, Toronto, Canada.

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis.In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/0846537120949974DOI Listing
February 2021

Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results.

PLoS One 2020 27;15(7):e0236182. Epub 2020 Jul 27.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.

Background: Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting.

Methods: Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models.

Results: Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively.

Conclusion: QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236182PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384762PMC
September 2020

Refractory Autoimmune Hemolytic Anemia and Pure Red Cell Aplasia Secondary to Immunotherapy Requiring Prolonged Immunosuppression.

JCO Oncol Pract 2020 10 30;16(10):699-700. Epub 2020 Jun 30.

University of Toronto, Toronto, Ontario, Canada.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1200/OP.20.00047DOI Listing
October 2020

Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study.

Cancer Med 2020 08 29;9(16):5798-5806. Epub 2020 Jun 29.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.

Background: This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics.

Methods: This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty-two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co-occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical-pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross-validation was performed using a leave-one-out cross-validation method.

Results: Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K-nearest neighbors (K-NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%.

Conclusion: QUS-based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cam4.3255DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7433820PMC
August 2020

The Hall technique of fame?

Authors:
Sonal Gandhi

Br Dent J 2020 02;228(3):161

Dental Core Trainee, Northwick Park Hospital, UK.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41415-020-1271-xDOI Listing
February 2020

Impact of Using Different Growth References on Interpretation of Anthropometric Parameters of Children Aged 8-15 Years.

Indian Pediatr 2020 02;57(2):124-128

Department of Pediatrics, Lady Hardinge Medical College and Kalawati Saran Children's Hospital, New Delhi, India. Correspondence to: Dr Anju Seth, Director Professor, Department of Pediatrics, Lady Hardinge Medical College and Kalawati Saran Children's Hospital, New Delhi 110 001, India.

Objective: To compare the effect of the application of three growth references (Agarwal, 1992; Indian Academy of Paediatrics (IAP), 2015; and World Health Organisation (WHO), 2007) on interpretation of anthropometric parameters in schoolchildren.

Setting: Cross-sectional school-based study.

Participants: Children 8-15 years studying in one government school and one private school of Delhi.

Procedure: The age- and gender-specific standard deviation scores of height-for-age and BMI-for-age were estimated for each student enrolled, using the three growth references independently.

Main Outcome Measure: The proportion of children with short stature, thinness and overweight/ obesity determined by each growth reference were compared.

Results: A total of 1237 students participated in the study. A significantly higher proportion of children (both sexes) were classified to have short stature using WHO 2007 reference (8.8%) as compared to the Agarwal (3.3%) charts and IAP, 2015 references (3.6%). The combined prevalence of overweight and obesity was highest (34.8%) by the IAP, 2015 reference as against 32% by Agarwal charts and 29.1% by WHO, 2007 reference. Good agreement existed between the IAP, 2015 reference and Agarwal charts in classifying subjects into different BMI categories (Kappa=0.82) and short stature (Kappa=0.99).

Conclusions: In view of differences noted, use of national population derived reference data is suggested to correctly define growth trajectories in children.
View Article and Find Full Text PDF

Download full-text PDF

Source
February 2020

Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models.

Transl Oncol 2019 Oct 17;12(10):1271-1281. Epub 2019 Jul 17.

Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada. Electronic address:

Purpose: The purpose of this study was to develop computational algorithms to best determine tumor responses early after the start of neoadjuvant chemotherapy, based on quantitative ultrasound (QUS) and textural analysis in patients with locally advanced breast cancer (LABC).

Methods: A total of 100 LABC patients treated with neoadjuvant chemotherapy were included in this study. Breast tumors were scanned with a clinical ultrasound system prior to treatment, during the first, fourth and eighth weeks of treatment, and prior to surgery. QUS parameters were calculated from ultrasound radio frequency data within tumor regions. Texture features were extracted from each QUS parametric map. Patients were classified into two groups based on identified clinical/pathological response: responders and non-responders. In order to differentiate treatment responders, three multi-feature response classification algorithms, namely a linear discriminant, a k-nearest-neighbor and a nonlinear support vector machine classifier were compared.

Results: All algorithms distinguished responders and non-responders with accuracies ranging between 68% and 92%. In particular, support vector machine performed the best in differentiating responders from non-responders with accuracies of 78%, 90% and 92% at weeks 1, 4 and 8 after the start of treatment, respectively. The most relevant features in separating the two response groups at early stages (weeks 1and 4) were texture features and at a later stage (week 8) were mean QUS parameters, particularly ultrasound backscatter intensity-based parameters.

Conclusion: An early stage treatment response prediction model developed by quantitative ultrasound and texture analysis combined with modern computational methods permits offering effective alternatives to standard treatment for refractory patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.tranon.2019.06.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6639683PMC
October 2019

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography.

Transl Oncol 2019 Sep 18;12(9):1177-1184. Epub 2019 Jun 18.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Department of Radiation Oncology, University of Toronto, Toronto, CA; Department of Medical Biophysics, University of Toronto, Toronto, CA; Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, CA; Physical Sciences, Sunnybrook Research Institute, Toronto, CA. Electronic address:

Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.tranon.2019.05.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586920PMC
September 2019

Trends in site of death and health care utilization at the end of life: a population-based cohort study.

CMAJ Open 2019 Apr-Jun;7(2):E306-E315. Epub 2019 Apr 26.

Departments of Critical Care Medicine (Hill, Scales, Rubenfeld, Wunsch, Dev, Fowler) and of Medicine (Gandhi, Berry), Sunnybrook Health Sciences Centre; Sunnybrook Research Institute (Hill); ICES (Stukel, Fu, Scales); Institute of Health Policy, Management and Evaluation (Stukel, Fowler), Interdepartmental Division of Critical Care (Scales, Rubenfeld, Wunsch, Dev, Fowler), Faculty of Medicine (Laupacis), Department of Anesthesia (Wunsch), Department of Medicine (Sinha, Gandhi, La Delfa), Division of Palliative Medicine, Department of Medicine (Zimmermann), and Division of Palliative Care, Department of Family Medicine, Faculty of Medicine (Myers), University of Toronto; Li Ka Shing Knowledge Institute (Laupacis) and Department of Palliative Care (La Delfa), St. Michael's Hospital, Toronto, Ont.; Departments of Medicine and Critical Care (Downar), Division of Palliative Care, University of Ottawa, Ottawa, Ont.; Divisions of Geriatric Medicine and Neurology (Rockwood), Department of Medicine, Dalhousie University, Halifax, NS; Department of Critical Care Medicine (Heyland), Queen's University, Kingston Ont.; Department of Medicine (Sinha) and Division of Palliative Care (Myers), Sinai Health System; Department of Medicine (Sinha), Division of Palliative Care, Department of Supportive Care (Zimmermann), and Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre (Ross), University Health Network, Toronto, Ont.; School of Population and Public Health (Kozak), University of British Columbia; Department of Family and Community Medicine (Kozak), Providence Health Care, Vancouver, BC.

Background: High rates of health care utilization at the end of life may be a marker of care that does not align with patient-stated preferences. We sought to describe trends in end-of-life care and factors associated with dying in hospital.

Methods: We conducted a population-level retrospective cohort study of adult decedents in Ontario between Apr. 1, 2004, and Mar. 31, 2015, using linked administrative data sets, including the Office of the Registrar General for Deaths database, the hospital Discharge Abstract Database, the National Ambulatory Care Reporting System and physicians' billing claims (Ontario Health Insurance Plan). The primary outcome was place of death. To determine health care utilization and health care costs during the 6 months before death, we also identified admissions to hospital and to the intensive care unit, emergency department visits, and receipt of mechanical ventilation and palliative care.

Results: In the last 6 months of life, 77.3% of 962 462 decedents presented to an emergency department, 68.4% were admitted to hospital, 19.4% were admitted to an intensive care unit, and 13.9% received mechanical ventilation. Forty-five percent of all deaths occurred in hospital, a proportion that declined marginally over time, whereas receipt of palliative care increased during terminal hospital admissions (from 14.0% in fiscal year 2004/05 to 29.3% in 2014/15, < 0.001) and in the last 6 months of life (from 28.1% in 2004/05 to 57.7% in 2014/15, < 0.001). The proportion of decedents who presented to the emergency department, were admitted to hospital or were admitted to the intensive care unit in the last 6 months of life did not change over 11 years. The mean total health care costs in the last 6 months of life were highest among those dying in hospital, with most costs attributable to inpatient medical care.

Interpretation: Health care utilization in the last 6 months of life was substantial and did not decrease over time. It is possible that increased capacity for palliative, hospice and home care at the end of life may help to better align health system resources with the preferences of most patients, a topic that should be explored in future studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.9778/cmajo.20180097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488480PMC
April 2019

User-Centered Design of a Web-Based Tool to Support Management of Chemotherapy-Related Toxicities in Cancer Patients.

J Med Internet Res 2019 03 28;21(3):e9958. Epub 2019 Mar 28.

University Health Network, Toronto, ON, Canada.

Background: Cancer patients receiving chemotherapy have high symptom needs that can negatively impact quality of life and result in high rates of unplanned acute care visits. Remote monitoring tools may improve symptom management in this patient population.

Objective: This study aimed to design a prototype tool to facilitate remote management of chemotherapy-related toxicities.

Methods: User needs were assessed using a participatory, user-centered design methodology that included field observation, interviews, and focus groups, and then analyzed using affinity diagramming. Participants included oncology patients, caregivers, and health care providers (HCPs) including medical oncologists, oncology nurses, primary care physicians, and pharmacists in Ontario, Canada. Overarching themes informed development of a Web-based prototype, which was further refined over 2 rounds of usability testing with end users.

Results: Overarching themes were derived from needs assessments, which included 14 patients, 1 caregiver, and 12 HCPs. Themes common to both patients and HCPs included gaps and barriers in current systems, need for decision aids, improved communication and options in care delivery, secure access to credible and timely information, and integration into existing systems. In addition, patients identified missed opportunities, care not meeting their needs, feeling overwhelmed and anxious, and wanting to be more empowered. HCPs identified accountability for patient management as an issue. These themes informed development of a Web-based prototype (bridges), which included toxicity tracking, self-management advice, and HCP communication functionalities. Usability testing with 11 patients and 11 HCPs was generally positive; however, identified challenges included tool integration into existing workflows, need for standardized toxicity self-management advice, issues of privacy and consent, and patient-tailored information.

Conclusions: Web-based tools integrating just-in-time self-management advice and HCP support into routine care may address gaps in systems for managing chemotherapy-related toxicities. Attention to the integration of new electronic tools into self-care by patients and practice was a strong theme for both patients and HCP participants and is a key issue that needs to be addressed for wide-scale adoption.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2196/jmir.9958DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458529PMC
March 2019

Ambulatory Toxicity Management (AToM) Pilot: results of a pilot study of a pro-active, telephone-based intervention to improve toxicity management during chemotherapy for breast cancer.

Pilot Feasibility Stud 2019 8;5:39. Epub 2019 Mar 8.

1Cancer Care Ontario, Toronto, ON Canada.

Background: Chemotherapy is associated with a significant risk of toxicity, which often peaks between ambulatory visits to the cancer centre. Remote symptom management support is a tool to optimize self-management and healthcare utilization, including emergency department visits and hospitalizations (ED+H) during chemotherapy. We performed a single-arm pilot study to evaluate the feasibility, acceptability, and potential impact of a telephone symptom management intervention on healthcare utilization during chemotherapy for early stage breast cancer (EBC).

Methods: Women starting adjuvant or neoadjuvant chemotherapy for EBC at two cancer centres in Ontario, Canada, received standardized, nurse-led calls to assess common toxicities at two time points following each chemotherapy administration. Feasibility outcomes included patient enrollment, retention, RN adherence to delivering calls per the study schedule, and resource use associated with calls; acceptability was evaluated based on patient and provider feedback. Impact on acute care utilization was evaluated post hoc by linking individual patient records to provincial data holdings to examine ED+H patterns among participating patients compared to contemporaneous controls.

Results: Between September 2013 and December 2014, 77 women were enrolled (mean age 55 years). Most commonly used regimens were AC-paclitaxel (58%) and FEC-docetaxel (16%); 78% of patients received primary granulocyte colony-stimulating factor prophylaxis. 83.8% of calls were delivered per schedule; mean call duration was 9 min. The intervention was well received by both patients and clinicians. Comparison of ED+H rates among study participants versus controls showed that there were fewer ED visits in intervention patients [incidence rate ratio (IRR) (95% CI) = 0.54 (0.36, 0.81)] but no difference in the rate of hospitalizations [IRR (95% CI) = 1.02 (0.59, 1.77)]. Main implementation challenges included identifying eligible patients, fitting the calls into existing clinical responsibilities, and effective communication to the patient's clinical team.

Conclusions: Telephone-based pro-active toxicity management during chemotherapy is feasible, perceived as valuable by clinicians and patients, and may be associated with lower rates of acute care use. However, attention must be paid to workflow issues for scalability. Larger scale evaluation of this approach is in progress.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s40814-019-0404-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6407231PMC
March 2019

Takotsubo Cardiomyopathy During Anti-HER2 Therapy for Metastatic Breast Cancer.

Oncologist 2019 02 2;24(2):e80-e82. Epub 2018 Nov 2.

Department of Medicine, University of Toronto, Toronto, Ontario, Canada.

Human epidermal growth factor receptor 2 (HER2)-targeted antibodies, including pertuzumab and trastuzumab, improve overall survival and progression-free survival among women with HER2-positive metastatic breast cancer, but grade ≥3 cardiotoxicity occurs in approximately 8% of cases. Here we report a case of Takotsubo cardiomyopathy associated with the use of dual anti-HER2 therapy in a 63-year-old woman who presented to the emergency department with an 8- to 10-hour history of progressive dyspnea after completing her third cycle of pertuzumab plus trastuzumab in addition to nab-paclitaxel chemotherapy. To our knowledge, this patient represents the first reported case of Takotsubo cardiomyopathy associated with pertuzumab plus trastuzumab combination therapy in the literature.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1634/theoncologist.2018-0285DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369941PMC
February 2019

Rare cause of gastric outlet obstruction.

J Community Hosp Intern Med Perspect 2018 23;8(4):250-251. Epub 2018 Aug 23.

Department of Gastroenterology, Greater Baltimore Medical Center, Baltimore, MD, USA.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/20009666.2018.1503924DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6116143PMC
August 2018

Rare cause of gastric outlet obstruction.

J Community Hosp Intern Med Perspect 2018 17;8(2):84-86. Epub 2018 Apr 17.

Department of Gastroenterology, Greater Baltimore Medical Center, Baltimore, MD, USA.

Bouveret's syndrome is a rare cause of gastric outlet obstruction. The stones enter the small bowel via cholecysto-enteric fistula. The most common presenting symptoms are abdominal pain, nausea and vomiting. The gold standard diagnostic test isesophagogastroduodenoscopy (EGD). Rigler's triad on abdominal x-ray is classic. CT scan findings are pneumobilia, cholecystoduodenal fistula and a gallstone in the duodenum. We present a case of a 75-year-old female who presents with 3 week history of nausea, vomiting, and diffuse abdominal pain. Initial presentation, imaging and EGD was concerning for malignancy. She was later diagnosed to have Bouveret's syndrome and underwent laparoscopic small bowel enterotomy with removal of gallstones.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/20009666.2018.1452517DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5906763PMC
April 2018

Perceived Barriers to Goals of Care Discussions With Patients With Advanced Cancer and Their Families in the Ambulatory Setting: A Multicenter Survey of Oncologists.

J Palliat Care 2018 Jul 2;33(3):125-142. Epub 2018 Apr 2.

2 Division of Medical Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Background: Earlier goals of care (GOC) discussions in patients with advanced cancer are associated with less aggressive end-of-life care including decreased use of medical technologies. Unfortunately, conversations often occur late in the disease trajectory when patients are acutely unwell. Here, we evaluate practitioner perspectives of patient, family, physician, and external barriers to early GOC discussions in the ambulatory oncology setting.

Methods: A previously published survey to assess barriers to GOC discussions among clinicians on inpatient medical wards was modified for the ambulatory oncology setting and distributed to oncologists from 12 centers in Ontario, Canada. Physicians were asked to rank the importance of various barriers to having GOC discussions (1 = extremely unimportant to 7 = extremely important).

Results: Questionnaires were completed by 30 (24%) of 127 physicians. Respondents perceived patient- and family-related factors as the most important barriers to GOC discussions. Of these, patient difficulty accepting prognosis or desire for aggressive treatment were perceived as most important. Patients' inflated expectation of treatment benefit was also considered an important barrier to discontinuing active cancer-directed therapy. While physician barriers were ranked lower than patient-related factors, clinicians' self-identified difficulty estimating prognosis and uncertainty regarding treatment benefits were also considered important. Patient's refusal for referral was the most highly rated barrier to early palliative care referral. Most respondents were nonetheless very or extremely willing to initiate (90%) or lead (87%) GOC discussions.

Conclusion: Oncologists ranked patient- and family-related factors as the most important barriers to GOC discussions, while clinicians' self-identified difficulty estimating prognosis and uncertainty regarding treatment benefits were also considered important. Further work is required to assess patient preferences and perceptions and develop targeted interventions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/0825859718762287DOI Listing
July 2018

Prospective Evaluation of the Impact of the 21-Gene Recurrence Score Assay on Adjuvant Treatment Decisions for Women with Node-Positive Breast Cancer in Ontario, Canada.

Oncologist 2018 07 25;23(7):768-775. Epub 2018 Jan 25.

Department of Medical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

Background: The 21-gene Recurrence Score (RS) assay is only reimbursed in Ontario for node-negative and micrometastatic node-positive (N+) early-stage breast cancer (EBC). We carried out a prospective study to evaluate the impact of the assay on treatment decisions for women with N+ EBC.

Subjects, Materials, And Methods: Women with estrogen receptor-positive, human epidermal growth receptor 2-negative EBC and one to three positive axillary lymph nodes, who were candidates for adjuvant chemotherapy in addition to hormonal treatment, but in whom the benefit of chemotherapy was uncertain, were eligible. The primary objective was to characterize how the results of the RS assay affected physicians' recommendations for adjuvant chemotherapy. Secondary objectives were to characterize changes in the physicians' and patients' level of confidence in treatment recommendations, to determine whether the results of the RS assay affected patients' treatment preferences, and to determine the final treatment administered.

Results: Seventy-two patients were recruited; the mean age was 61. RS was <18 in 55%, between 18 and 30 in 36%, and ≥31 in 9% of patients. Treatment recommendations changed in 36% of all evaluable patients. The most significant change was in the group with a low RS. Physicians' and patients' confidence in treatment recommendations increased in 49% and 54% of cases, respectively. Upfront chemotherapy was recommended to 79% of patients before the assay; 42% ultimately received chemotherapy.

Conclusion: The RS assay resulted in a substantial decrease in the number of patients who received chemotherapy and in an increase in physicians' and patients' confidence in the adjuvant treatment recommendations.

Implications For Practice: This is the first decision impact study to include exclusively women with ER-positive, HER2-negative, early-stage breast cancer with 1-3 positive lymph nodes, a population typically treated with adjuvant chemotherapy. This study provides evidence that, in these patients, the Oncotype Dx Recurrence Score assay influences systemic treatment decisions. Most of the changes in treatment recommendation resulted in withdrawal of chemotherapy or change in recommendation from a chemotherapy regimen with anthracyclines to a taxane-only regimen. If prospective studies confirm that these decisions result in good outcomes, a reduction in the use of chemotherapy might result in pharmacoeconomic savings.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1634/theoncologist.2017-0346DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6058344PMC
July 2018

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features.

PLoS One 2018 3;13(1):e0189634. Epub 2018 Jan 3.

Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.

Background: Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer.

Methods: The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times.

Results: Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology.

Conclusions: This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189634PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751990PMC
February 2018

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities.

Sci Rep 2017 09 4;7(1):10352. Epub 2017 Sep 4.

Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.

Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-017-09678-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583340PMC
September 2017

Use of Adjuvant Bisphosphonates and Other Bone-Modifying Agents in Breast Cancer: A Cancer Care Ontario and American Society of Clinical Oncology Clinical Practice Guideline.

J Clin Oncol 2017 Jun 6;35(18):2062-2081. Epub 2017 Mar 6.

Sukhbinder Dhesy-Thind, Juravinski Cancer Centre; Sukhbinder Dhesy-Thind and Glenn G. Fletcher, McMaster University, Hamilton, Ontario; Phillip S. Blanchette, Sunnybrook Odette Cancer Centre; Sonal Gandhi, Sunnybrook Health Sciences, Toronto, Ontario; Mark J. Clemons, The Ottawa Hospital Cancer Centre, Ottawa, Ontario; Rasna Gupta, Windsor Regional Cancer Program, Windsor, Ontario; Mihaela Mates, Kingston General Hospital, Kingston, Ontario; Ted Vandenberg, London Health Sciences Centre, London, Ontario, Canada; Melissa S. Dillmon, Harbin Clinic, Rome, GA; Elizabeth S. Frank, Lexington; Beverly Moy, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA; and Catherine H. Van Poznak, University of Michigan, Ann Arbor, MI.

Purpose To make recommendations regarding the use of bisphosphonates and other bone-modifying agents as adjuvant therapy for patients with breast cancer. Methods Cancer Care Ontario and ASCO convened a Working Group and Expert Panel to develop evidence-based recommendations informed by a systematic review of the literature. Results Adjuvant bisphosphonates were found to reduce bone recurrence and improve survival in postmenopausal patients with nonmetastatic breast cancer. In this guideline, postmenopausal includes patients with natural menopause or that induced by ovarian suppression or ablation. Absolute benefit is greater in patients who are at higher risk of recurrence, and almost all trials were conducted in patients who also received systemic therapy. Most studies evaluated zoledronic acid or clodronate, and data are extremely limited for other bisphosphonates. While denosumab was found to reduce fractures, long-term survival data are still required. Recommendations It is recommended that, if available, zoledronic acid (4 mg intravenously every 6 months) or clodronate (1,600 mg/d orally) be considered as adjuvant therapy for postmenopausal patients with breast cancer who are deemed candidates for adjuvant systemic therapy. Further research comparing different bone-modifying agents, doses, dosing intervals, and durations is required. Risk factors for osteonecrosis of the jaw and renal impairment should be assessed, and any pending dental or oral health problems should be dealt with prior to starting treatment. Data for adjuvant denosumab look promising but are currently insufficient to make any recommendation. Use of these agents to reduce fragility fractures in patients with low bone mineral density is beyond the scope of the guideline. Recommendations are not meant to restrict such use of bone-modifying agents in these situations. Additional information at www.asco.org/breast-cancer-adjuvant-bisphosphonates-guideline , www.asco.org/guidelineswiki , https://www.cancercareontario.ca/guidelines-advice/types-of-cancer/breast .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1200/JCO.2016.70.7257DOI Listing
June 2017

The role of quantitative estrogen receptor status in predicting tumor response at surgery in breast cancer patients treated with neoadjuvant chemotherapy.

Breast Cancer Res Treat 2017 Jul 2;164(2):285-294. Epub 2017 May 2.

Institute of Health Policy, Management and Evaluation, Toronto, ON, M5T 3M6, Canada.

Purpose: Estrogen receptor (ER) negative (-) breast cancer (BC) patients have better tumor response rates than ER-positive (+) patients after neoadjuvant chemotherapy (NCT). We conducted a retrospective review using the institutional database "Biomatrix" to assess the value of quantitative ER status in predicting tumor response at surgery and to identify potential predictors of survival outcomes.

Methods: Univariate followed by multivariable regression analyses were conducted to assess the association between quantitative ER and tumor response assessed as tumor size reduction and pathologic complete response (pCR). Predictors of recurrence-free survival (RFS) were identified using a cox proportional hazards model (CPH). A log-rank test was used to compare RFS between groups if a significant predictor was identified.

Results: 304 patients were included with a median follow-up of 43.3 months (Q1-Q3 28.7-61.1) and a mean age of 49.7 years (SD 10.9). Quantitative ER was inversely associated with tumor size reduction and pCR (OR 0.99, 95% CI 0.99-1.00, p = 0.027 and 0.98 95% CI 0.97-0.99, p < 0.0001, respectively). A cut-off of 60 and 80% predicted best the association with tumor size reduction and pCR, respectively. pCR was shown to be an independent predictor of RFS (HR 0.17, 95% CI 0.07-0.43, p = 0.0002) in all patients. At 5 years, 93% of patients with pCR and 72% of patients with residual tumor were recurrence-free, respectively (p = 0.0012).

Conclusions: Quantitative ER status is inversely associated with tumor response in BC patients treated with NCT. A cut-off of 60 and 80% predicts best the association with tumor size reduction and pCR, respectively. Therefore, patients with an ER status higher than the cut-off might benefit from a neoadjuvant endocrine therapy approach. Patients with pCR had better survival outcomes independently of their tumor phenotype. Further prospective studies are needed to validate the clinical utility of quantitative ER as a predictive marker of tumor response.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10549-017-4269-6DOI Listing
July 2017

Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis.

Br J Cancer 2017 May 18;116(10):1329-1339. Epub 2017 Apr 18.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada.

Background: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pretreatment DOS functional maps for predicting LABC response to NAC.

Methods: Locally advanced breast cancer patients (n=37) underwent DOS breast imaging before starting NAC. Breast tissue parametric maps were constructed and texture analyses were performed based on grey-level co-occurrence matrices for feature extraction. Ground truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS textural features computed on volumetric tumour data before the start of treatment (i.e., 'pretreatment') to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naive Bayes, and k-nearest neighbour classifiers.

Results: Data indicated that textural characteristics of pretreatment DOS parametric maps can differentiate between treatment response outcomes. The HbO homogeneity resulted in the highest accuracy among univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5% and 89.0%, respectively, and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-contrast+HbO-homogeneity, which resulted in a %Sn/%Sp=78.0/81.0% and an accuracy of 79.5%.

Conclusions: This study demonstrated that the pretreatment DOS texture features can predict breast cancer response to NAC and potentially guide treatments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/bjc.2017.97DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482739PMC
May 2017

A single-institution experience of salvage therapy for patients with early and locally advanced breast cancer who progress during neoadjuvant chemotherapy.

Breast Cancer Res Treat 2017 May 27;163(1):11-19. Epub 2017 Feb 27.

Medical Oncology Division, Department of Medicine, Sunnybrook Odette Cancer Centre, 2075 Bayview Avenue, Toronto, ON, M4N3M5, Canada.

Purpose: Progression during neoadjuvant chemotherapy (NAT) for early and locally advanced breast cancer is generally uncommon. However, these patients tend to do poorly, and salvage therapy (ST) use is variable and often not well defined. We aimed to establish the characteristics and outcomes of breast cancer (BC) patients progressing on NAT, report the patterns of institutional ST usage, and identify predictors of ST failure.

Methods: A retrospective review was conducted using the "Biomatrix" institutional database. Fisher's exact test was used to study the association between baseline characteristics and progression after ST. Survival outcomes were estimated using Kaplan-Meier. Disease-Free Survival 1 (DFS1) and DFS2 represent the time between diagnosis and first progression, and the first and second progression, respectively. The log-rank test was used to compare survival outcomes between different ST types.

Results: Thirty patients out of 413 (7.2%) progressed on primary NAT, with a median follow-up of 28.52 months (13.77-46.97) and a mean age of 57 years (standard deviation: 12). The two most frequently used ST modalities were surgery (43%) and radiation with concurrent cisplatin chemotherapy (CT/RT) (40%). Eighty percent of the patients made it to subsequent surgery and among those, 11 (69%) were initially not operable and their tumors were rendered surgically removable after ST. The initial tumor stage and grade, and the presence of lymphovascular invasion predicted progression after ST (p = 0.02, p = 0.03 and p = 0.01, respectively). Median DFS1, DFS2, and overall survival were 4.4 months (95% CI 3.6-5.7), 14.8 months (95% CI 2.37-NR), and 39.5 months (95% CI 22.73-NR), respectively. No difference in survival outcomes based on ST type was seen.

Conclusion: In this evaluated cohort and despite potential poorer outcomes, patients progressing on NAT responded well to ST, became operable, and had promising survival outcomes. Appropriate selection of ST is crucial, and can help improve outcomes in such patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10549-017-4167-yDOI Listing
May 2017
-->