Publications by authors named "Sanjay Aneja"

39 Publications

Comparison of radiomic feature aggregation methods for patients with multiple tumors.

Sci Rep 2021 May 7;11(1):9758. Epub 2021 May 7.

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, USA.

Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.
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http://dx.doi.org/10.1038/s41598-021-89114-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105371PMC
May 2021

Impact of tissue heterogeneity correction on Gamma Knife stereotactic radiosurgery of acoustic neuromas.

J Radiosurg SBRT 2021 ;7(3):207-212

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA.

Purpose/objectives: Treatment planning systems (TPS) for Gamma Knife stereotactic radiosurgery (GK-SRS) include TMR10 algorithms, which assumes tissue homogeneity equivalent to water, and collapsed-cone convolutional (CCC) algorithms, which accounts for tissue inhomogeneity. This study investigated dosimetric differences between TMR10 and CCC TPS for acoustic neuromas (ANs) treated with GK-SRS.

Materials/methods: A retrospective review of 56 AN treated with GK-SRS was performed. All patients underwent MRI and CT imaging during their initial treatment and were planned using TMR10. Each plan was recalculated with CCC using electron density extracted from CT. Parameters of interest included D, D, D, cochlea D, mean cochlea dose, target size, and laterality (>20 mm from central axis).

Results: Median target volume of patients was 1.5 cc (0.3 cc-2.8 cc) with median dose of 12 Gy prescribed to the 50% isodose line. Compared to CCC algorithms, the TMR10 calculated dose was higher: D was higher by an average 6.2% (p < 0.001), D was higher by an average 3.1% (p < 0.032), D was higher by an average of 11.3%. For lateralized targets, calculated D and D were higher by 7.1% (p < 0.001) and 10.6% (p < 0.001), respectively. For targets <1 cc, D and D were higher by 8.9% (p ≤ 0.009) and 12.1% (p ≤ 0.001), respectively. Cochlea D was higher, by an average of 20.1% (p < 0.001).

Conclusion: There was a statistically significant dosimetric differences observed between TMR10 and CCC algorithms for AN GK-SRS, particularly in small and lateralized ANs. It may be important to note these differences when relating GK-SRS with standard heterogeneity-corrected SRS regimens.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055239PMC
January 2021

Public vs physician views of liability for artificial intelligence in health care.

J Am Med Inform Assoc 2021 Apr 19. Epub 2021 Apr 19.

Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut, USA.

The growing use of artificial intelligence (AI) in health care has raised questions about who should be held liable for medical errors that result from care delivered jointly by physicians and algorithms. In this survey study comparing views of physicians and the U.S. public, we find that the public is significantly more likely to believe that physicians should be held responsible when an error occurs during care delivered with medical AI, though the majority of both physicians and the public hold this view (66.0% vs 57.3%; P = .020). Physicians are more likely than the public to believe that vendors (43.8% vs 32.9%; P = .004) and healthcare organizations should be liable for AI-related medical errors (29.2% vs 22.6%; P = .05). Views of medical liability did not differ by clinical specialty. Among the general public, younger people are more likely to hold nearly all parties liable.
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http://dx.doi.org/10.1093/jamia/ocab055DOI Listing
April 2021

Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival.

JAMA Netw Open 2021 03 1;4(3):e211793. Epub 2021 Mar 1.

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut.

Importance: Cancer registries are important real-world data sources consisting of data abstraction from the medical record; however, patients with unknown or missing data are underrepresented in studies that use such data sources.

Objective: To assess the prevalence of missing data and its association with overall survival among patients with cancer.

Design, Setting, And Participants: In this retrospective cohort study, all variables within the National Cancer Database were reviewed for missing or unknown values for patients with the 3 most common cancers in the US who received diagnoses from January 1, 2006, to December 31, 2015. The prevalence of patient records with missing data and the association with overall survival were assessed. Data analysis was performed from February to August 2020.

Exposures: Any missing data field within a patient record among 63 variables of interest from more than 130 total variables in the National Cancer Database.

Main Outcomes And Measures: Prevalence of missing data in the medical records of patients with cancer and associated 2-year overall survival.

Results: A total of 1 198 749 patients with non-small cell lung cancer (mean [SD] age, 68.5 [10.9] years; 628 811 men [52.5%]), 2 120 775 patients with breast cancer (mean [SD] age, 61.0 [13.3] years; 2 101 758 women [99.1%]), and 1 158 635 patients with prostate cancer (mean [SD] age, 65.2 [9.0] years; 100% men) were included in the analysis. Among those with non-small cell lung cancer, 851 295 patients (71.0%) were missing data for variables of interest; 2-year overall survival was 33.2% for patients with missing data and 51.6% for patients with complete data (P < .001). Among those with breast cancer, 1 161 096 patients (54.7%) were missing data for variables of interest; 2-year overall survival was 93.2% for patients with missing data and 93.9% for patients with complete data (P < .001). Among those with prostate cancer, 460 167 patients (39.7%) were missing data for variables of interest; 2-year overall survival was 91.0% for patients with missing data and 95.6% for patients with complete data (P < .001).

Conclusions And Relevance: This study found that within a large cancer registry-based real-world data source, there was a high prevalence of missing data that were unable to be ascertained from the medical record. The prevalence of missing data among patients with cancer was associated with heterogeneous differences in overall survival. Improvements in documentation and data quality are necessary to make optimal use of real-world data for clinical advancements.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.1793DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988369PMC
March 2021

Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors.

medRxiv 2020 Nov 6. Epub 2020 Nov 6.

Department of Therapeutic Radiology, Yale School of Medicine.

Background: Radiomic feature analysis has been shown to be effective at modeling cancer outcomes. It has not yet been established how to best combine these radiomic features in patients with multifocal disease. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognostication to better inform treatment.

Methods: We compared six mathematical methods of combining radiomic features of 3596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest.

Results: Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model.

Conclusions: Radiomic features can be effectively combined to establish patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and disease sites.
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http://dx.doi.org/10.1101/2020.11.04.20226159DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654896PMC
November 2020

Multi-institutional retrospective review of stereotactic radiosurgery for brain metastasis in patients with small cell lung cancer without prior brain-directed radiotherapy.

J Radiosurg SBRT 2020 ;7(1):19-27

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06511, USA.

Patients with small cell lung cancer (SCLC) brain metastasis (BM) typically receive whole brain radiotherapy (WBRT) as data regarding upfront radiosurgery (SRS) in this setting are sparse. Patients receiving SRS for SCLC BM without prior brain radiation were identified at three U.S. institutions. Overall survival (OS), freedom from intracranial progression (FFIP), freedom from WBRT (FFWBRT), and freedom from neurologic death (FFND) were determined from time of SRS. Thirty-three patients were included with a median of 2 BM (IQR 1-6). Median OS and FFIP were 6.7 and 5.8 months, respectively. Median FFIP for patients with ≤2 versus >2 BM was 7.1 versus 3.6 months, p=0.0303. Eight patients received salvage WBRT and the 6-month FFWBRT and FFND were 87.8%. and 90.1%, respectively. Most SCLC patients with BM who received upfront SRS avoided WBRT and neurologic death, suggesting that SRS may be an option in select patients.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406345PMC
January 2020

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation.

Pract Radiat Oncol 2021 Jan-Feb;11(1):74-83. Epub 2020 Jun 13.

Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.

Purpose: Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area.

Methods And Materials: The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI.

Results: In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities.

Conclusion: Together, these action points can facilitate the translation of AI into clinical practice.
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http://dx.doi.org/10.1016/j.prro.2020.06.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293478PMC
June 2020

Reply to A.B. Simon et al.

J Clin Oncol 2020 06 9;38(16):1869-1870. Epub 2020 Apr 9.

Benjamin H. Kann, MD, Department of Radiation Oncology, Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, and Artificial Intelligence in Medicine Program, Brigham and Women's Hospital, Boston, MA; Sam Payabvash, MD, Department of Radiology, Yale School of Medicine, New Haven, CT; and Sanjay Aneja, MD, Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.

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http://dx.doi.org/10.1200/JCO.20.00402DOI Listing
June 2020

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.

J Clin Oncol 2020 04 9;38(12):1304-1311. Epub 2019 Dec 9.

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.

Purpose: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians.

Methods: We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists.

Results: A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists' AUCs of 0.70 and 0.71 ( = .02 and = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 ( < .0001 and = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance.

Conclusion: Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.
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http://dx.doi.org/10.1200/JCO.19.02031DOI Listing
April 2020

Imaging biomarkers for brain metastases: more than meets the eye.

Neuro Oncol 2019 12;21(12):1493-1494

Yale Brain Tumor Center, Yale Cancer Center and Smilow Cancer Hospital, New Haven, Connecticut.

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http://dx.doi.org/10.1093/neuonc/noz193DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917408PMC
December 2019

Applications of artificial intelligence in neuro-oncology.

Curr Opin Neurol 2019 12;32(6):850-856

Yale Brain Tumor Center at Yale Cancer Center and Smilow Cancer Hospital.

Purpose Of Review: To discuss recent applications of artificial intelligence within the field of neuro-oncology and highlight emerging challenges in integrating artificial intelligence within clinical practice.

Recent Findings: In the field of image analysis, artificial intelligence has shown promise in aiding clinicians with incorporating an increasing amount of data in genomics, detection, diagnosis, classification, risk stratification, prognosis, and treatment response. Artificial intelligence has also been applied in epigenetics, pathology, and natural language processing.

Summary: Although nascent, applications of artificial intelligence within neuro-oncology show significant promise. Artificial intelligence algorithms will likely improve our understanding of brain tumors and help drive future innovations in neuro-oncology.
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http://dx.doi.org/10.1097/WCO.0000000000000761DOI Listing
December 2019

Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks.

Sci Rep 2018 09 19;8(1):14036. Epub 2018 Sep 19.

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, USA.

Identification of nodal metastasis and tumor extranodal extension (ENE) is crucial for head and neck cancer management, but currently only can be diagnosed via postoperative pathology. Pretreatment, radiographic identification of ENE, in particular, has proven extremely difficult for clinicians, but would be greatly influential in guiding patient management. Here, we show that a deep learning convolutional neural network can be trained to identify nodal metastasis and ENE with excellent performance that surpasses what human clinicians have historically achieved. We trained a 3-dimensional convolutional neural network using a dataset of 2,875 CT-segmented lymph node samples with correlating pathology labels, cross-validated and fine-tuned on 124 samples, and conducted testing on a blinded test set of 131 samples. On the blinded test set, the model predicted ENE and nodal metastasis each with area under the receiver operating characteristic curve (AUC) of 0.91 (95%CI: 0.85-0.97). The model has the potential for use as a clinical decision-making tool to help guide head and neck cancer patient management.
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http://dx.doi.org/10.1038/s41598-018-32441-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145900PMC
September 2018

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Radiother Oncol 2018 12 12;129(3):421-426. Epub 2018 Jun 12.

Oregon Health & Science University, Portland, USA.

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.
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http://dx.doi.org/10.1016/j.radonc.2018.05.030DOI Listing
December 2018

Impact of Health Insurance Status on Prostate Cancer Treatment Modality Selection in the United States.

Am J Clin Oncol 2018 Nov;41(11):1062-1068

*Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT †Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.

Objectives: A variety of treatment modalities are available for the management of clinically localized prostate cancer in the United States. In addition to clinical factors, treatment modality choice may be influenced by a patient's insurance status. Using a national data set, we investigated the relationship between insurance status and prostate cancer treatment modality selection among nonelderly men in the United States.

Methods: Nonelderly men age 18 to 64 years treated for localized prostate cancer from 2010 to 2014 were identified within the National Cancer Database. Patients with no insurance, Medicaid, or private insurance were included. The χ and multivariable logistic regression analyses were used to evaluate the association of insurance status, other demographic and facility factors, and D'Amico risk classification with treatment modality.

Results: We identified 135,937 patients with either no insurance (2.8%), Medicaid (4.2%), or private insurance (92.9%) treated for prostate cancer who underwent cancer-directed treatment or active surveillance between 2010 and 2014. Patients with private insurance were more likely to receive minimally invasive surgery (61.4% vs. 35.4%, respectively; P<0.001) and less likely to receive external beam radiotherapy (10.9% vs. 26.9%, respectively; P<0.001) than patients with no insurance. On multivariable analysis, among patients with no insurance and private insurance, private insurance was the strongest predictor of receipt of minimally invasive surgery (adjusted odds ratio, 2.61; 95% confidence interval, 2.44-2.79; P<0.001).

Conclusion: Insurance status is a strong predictor of prostate cancer treatment modality among nonelderly men in the United States.
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http://dx.doi.org/10.1097/COC.0000000000000423DOI Listing
November 2018

MRI-Ultrasound Fusion Targeted Biopsy of Prostate Imaging Reporting and Data System Version 2 Category 5 Lesions Found False-Positive at Multiparametric Prostate MRI.

AJR Am J Roentgenol 2018 May 28;210(5):W218-W225. Epub 2018 Feb 28.

1 Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT.

Objective: The purpose of this study was to determine imaging and clinical features associated with Prostate Imaging Reporting and Data System (PI-RADS) category 5 lesions identified prospectively at multiparametric MRI (mpMRI) that were found benign at MRI-ultrasound fusion targeted biopsy.

Materials And Methods: Between January 2015 and July 2016, 325 men underwent prostate mpMRI followed by MRI-ultrasound fusion targeted biopsy of 420 lesions prospectively identified and assessed with PI-RADS version 2. The frequency of clinically significant prostate cancer (defined as Gleason score ≥ 7) among PI-RADS 5 lesions was determined. Lesions with benign pathologic results were retrospectively reassessed by three abdominal radiologists and categorized as concordant or discordant between mpMRI and biopsy results. Multivariate logistic regression was used to identify factors associated with benign disease. Bonferroni correction was used.

Results: Of the 98 PI-RADS 5 lesions identified in 89 patients, 18% (18/98) were benign, 10% (10/98) were Gleason 6 disease, and 71% (70/98) were clinically significant prostate cancer. Factors associated with benign disease at multivariate analysis were lower prostate-specific antigen density (odds ratio [OR], 0.88; p < 0.001) and apex (OR, 3.54; p = 0.001) or base (OR, 7.11; p = 0.012) location. On secondary review of the 18 lesions with benign pathologic results, 39% (7/18) were scored as benign prostatic hyperplasia nodules, 28% (5/18) as inflammatory changes, 5% (1/18) as normal anatomic structures, and 28% (5/18) as discordant with imaging findings.

Conclusion: PI-RADS 5 lesions identified during routine clinical interpretation are associated with a high risk of clinically significant prostate cancer. A benign pathologic result was significantly correlated with lower prostate-specific antigen density and apex or base location and most commonly attributed to a benign prostatic hyperplasia nodule. Integration of these clinical features may improve the interpretation of high-risk lesions identified with mpMRI.
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http://dx.doi.org/10.2214/AJR.17.18680DOI Listing
May 2018

Risk of Clinically Significant Prostate Cancer Associated With Prostate Imaging Reporting and Data System Category 3 (Equivocal) Lesions Identified on Multiparametric Prostate MRI.

AJR Am J Roentgenol 2018 Feb 7;210(2):347-357. Epub 2017 Nov 7.

1 Department of Radiology, University of Colorado, 12401 E 17th Ave, Mail Stop L954, Aurora, CO 80045.

Objective: The objective of this study is to determine the frequency of clinically significant cancer (CSC) in Prostate Imaging Reporting and Data System (PI-RADS) category 3 (equivocal) lesions prospectively identified on multiparametric prostate MRI and to identify risk factors (RFs) for CSC that may aid in decision making.

Materials And Methods: Between January 2015 and July 2016, a total of 977 consecutively seen men underwent multiparametric prostate MRI, and 342 underwent MRI-ultrasound (US) fusion targeted biopsy. A total of 474 lesions were retrospectively reviewed, and 111 were scored as PI-RADS category 3 and were visualized using a 3-T MRI scanner. Multiparametric prostate MR images were prospectively interpreted by body subspecialty radiologists trained to use PI-RADS version 2. CSC was defined as a Gleason score of at least 7 on targeted biopsy. A multivariate logistic regression model was constructed to identify the RFs associated with CSC.

Results: Of the 111 PI-RADS category 3 lesions, 81 (73.0%) were benign, 11 (9.9%) were clinically insignificant (Gleason score, 6), and 19 (17.1%) were clinically significant. On multivariate analysis, three RFs were identified as significant predictors of CSC: older patient age (odds ratio [OR], 1.13; p = 0.002), smaller prostate volume (OR, 0.94; p = 0.008), and abnormal digital rectal examination (DRE) findings (OR, 3.92; p = 0.03). For PI-RADS category 3 lesions associated with zero, one, two, or three RFs, the risk of CSC was 4%, 16%, 62%, and 100%, respectively. PI-RADS category 3 lesions for which two or more RFs were noted (e.g., age ≥ 70 years, gland size ≤ 36 mL, or abnormal DRE findings) had a CSC detection rate of 67% with a sensitivity of 53%, a specificity of 95%, a positive predictive value of 67%, and a negative predictive value of 91%.

Conclusion: Incorporating clinical parameters into risk stratification algorithms may improve the ability to detect clinically significant disease among PI-RADS category 3 lesions and may aid in the decision to perform biopsy.
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http://dx.doi.org/10.2214/AJR.17.18516DOI Listing
February 2018

Annual Facility Treatment Volume and Patient Survival for Mycosis Fungoides and Sézary Syndrome.

Clin Lymphoma Myeloma Leuk 2017 08 24;17(8):520-526.e2. Epub 2017 Jun 24.

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.

Background: Management of mycosis fungoides and Sézary syndrome (MF/SS) is complex, and randomized evidence to guide treatment is lacking. The institutional treatment volumes for MF/SS might vary widely nationally and influence patient survival.

Patients And Methods: Using the National Cancer Database, we identified patients with a diagnosis of MF/SS from 2004 to 2011 in the United States who had received treatment at a reporting facility. The patients were grouped into quintiles according to their treatment facility's average annual treatment volume (ATV). The characteristics associated with ATV were identified and compared using χ tests. Overall survival (OS) was compared among the ATV quintiles using the Kaplan-Meier method with log-rank tests and multivariable Cox regression with hazard ratios (HRs). OS was also analyzed using the annual patient volume as a continuous variable.

Results: A total of 2205 patients treated at 374 facilities were included for analysis. The ATV quintile cutoffs were 1, 3, 6, and 9 patients. With a median follow-up period of 59 months, the 5-year estimated OS survival increased with ATV from 56.7% in the lowest quintile (≤ 1 patient annually) to 83.8% in the highest quintile (> 9 patients annually; P < .001). On multivariable analysis, greater ATV was associated with improved survival when analyzed as a continuous variable (HR, 0.96 per patient per year; 95% confidence interval, 0.94-0.98; P < .001) and when comparing the highest quintile to the lowest quintile (HR, 0.46; 95% confidence interval, 0.39-0.55).

Conclusion: The present national database analysis demonstrated that higher facility ATV is associated with improved OS for patients with MF/SS. Further study is needed to determine the underlying reasons for improved survival with higher facility ATV.
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http://dx.doi.org/10.1016/j.clml.2017.05.017DOI Listing
August 2017

Concurrent chemoradiotherapy versus radiotherapy alone for "biopsy-only" glioblastoma multiforme.

Cancer 2016 08 12;122(15):2364-70. Epub 2016 May 12.

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut.

Background: Combined temozolomide and radiotherapy (RT) is the standard postoperative therapy for glioblastoma multiforme (GBM). However, the clearest benefit of concurrent chemoradiotherapy (CRT) observed in clinical trials has been among patients who undergo surgical resection. Whether the improved survival with CRT extends to patients who undergo "biopsy only" is less certain. The authors compared overall survival (OS) in a national cohort of patients with GBM who underwent biopsy and received either RT alone or CRT during the temozolomide era.

Methods: The US National Cancer Data Base was used to identify patients with histologically confirmed, biopsy-only GBM who received either RT alone or CRT from 2006 through 2011. Demographic and clinicopathologic predictors of treatment were analyzed using the chi-square test, the t test, and multivariable logistic regression. OS was evaluated using the log-rank test, multivariable Cox proportional hazard regression, and propensity score-matched analysis.

Results: In total, 1479 patients with biopsy-only GBM were included, among whom 154 (10.4%) received RT alone and 1325 (89.6%) received CRT. The median age at diagnosis was 61 years. CRT was associated with a significant OS benefit compared with RT alone (median, 9.2 vs 5.6 months; hazard ratio [HR], 0.64; 95% confidence interval [CI], 0.54-0.76; P < .001). CRT was independently associated with improved OS compared with RT alone on multivariable analysis (HR, 0.71; 95% CI, 0.60-0.85; P < .001). A significant OS benefit for CRT persisted in a propensity score-matched analysis (HR, 0.72; 95% CI, 0.56-0.93; P = .009).

Conclusions: The current data suggest that CRT significantly improves OS in patients with GBM who undergo biopsy only compared with RT alone and should remain the standard of care for patients who can tolerate therapy. Cancer 2016;122:2364-2370. © 2016 American Cancer Society.
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http://dx.doi.org/10.1002/cncr.30063DOI Listing
August 2016

A PHASE II TRIAL OF BALLOON-CATHETER PARTIAL BREAST BRACHYTHERAPY OPTIMIZATION IN THE TREATMENT OF STAGE 0, I AND IIA BREAST CARCINOMA.

J Radiat Oncol 2014 Dec;3(4):371-378

Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT.

Objectives: (a) To prospectively determine if multidwell position dose delivery can decrease skin dose and resultant toxicity over single dwell balloon-catheter partial breast irradiation, and (b) to evaluate whether specific skin parameters could be safely used instead of skin-balloon distance alone for predicting toxicity and treatment eligibility.

Methods: A single-arm phase II study using a Simon two-stage design was performed on 28 women with stage 0-II breast cancer. All patients were treated with multiple dwell position balloon-catheter brachytherapy. The primary endpoint was ≥ grade 2 skin toxicity. Initial entry required a balloon-skin distance ≥ 7 mm. Based on the toxicity in the first 16 patients, additional patients were treated irrespective of skin-balloon distance as long as the Dmax to 1 mm skin thickness was < 130%.

Results: Compared to the phantom single dwell plans, multidwell planning yielded superior PTV coverage as per median V90, V95 and V100, but had slightly worse V150, V200 and DHI. Dmax to skin was decreased by multidwell planning at multiple skin thicknesses. The most common acute toxicity was grade 1 erythema (57%), and only two patients (7%) developed acute grade 2 toxicity (erythema). Late grade 1 fibrosis was seen in 32%. No patients experienced grade 3, 4, or 5 toxicity.

Conclusions: Multidwell position planning for balloon-catheter brachytherapy results in lower skin doses with equal to superior PTV coverage and an overall low rate of initial skin toxicity. Our data suggest that limiting the Dmax to < 130% to 1 mm thick skin is achievable and results in minimal toxicity.
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http://dx.doi.org/10.1007/s13566-014-0153-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254816PMC
December 2014

Differences in Funding Sources of Phase III Oncology Clinical Trials by Treatment Modality and Cancer Type.

Am J Clin Oncol 2017 Jun;40(3):312-317

*Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT †Department of Radiation Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT.

Objectives: Given the limited resources available to conduct clinical trials, it is important to understand how trial sponsorship differs among different therapeutic modalities and cancer types and to consider the ramifications of these differences.

Methods: We searched clinicaltrials.gov for a cross-sectional register of active, phase III, randomized controlled trials (RCTs) studying treatment-related endpoints such as survival and recurrence for the 24 most prevalent malignancies. We classified the RCTs into 7 categories of therapeutic modality: (1) chemotherapy/other cancer-directed drugs, (2) targeted therapy, (3) surgery, (4) radiation therapy (RT), (5) RT with other modalities, (6) multimodality therapy without RT, and (7) other. RCTs were categorized as being funded by one or more of the following groups: (1) government, (2) hospital/university, (3) industry, and (4) other. χ analysis was performed to detect differences in funding source distribution between modalities and cancer types.

Results: The percentage of multimodality trials (5%) and radiation RCTs (4%) funded by industry was less than that for chemotherapy (32%, P<0.01) or targeted therapy (48%, P<0.01). Trials studying targeted therapy were less likely to have hospital/university funding than any of the other modalities (P<0.01 in each comparison). Trials of chemotherapy were more likely to be funded by industry if they also studied targeted therapy (P<0.01).

Conclusion: RCTs studying targeted therapies are more likely to be funded by industry than trials studying multimodality therapy or radiation. The impact of industry funding versus institutional or governmental sources of funding for cancer research is unclear and requires further study.
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http://dx.doi.org/10.1097/COC.0000000000000152DOI Listing
June 2017

Comparative effectiveness research in radiation oncology: stereotactic radiosurgery, hypofractionation, and brachytherapy.

Semin Radiat Oncol 2014 Jan;24(1):35-42

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT; Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT; Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale School of Medicine, New Haven, CT.

Radiation oncology encompasses a diverse spectrum of treatment modalities, including stereotactic radiosurgery, hypofractionated radiotherapy, and brachytherapy. Though all these modalities generally aim to do the same thing-treat cancer with therapeutic doses of radiation while relatively sparing normal tissue from excessive toxicity, the general radiobiology and physics underlying each modality are distinct enough that their equivalence is not a given. Given the continued innovation in radiation oncology, the comparative effectiveness of these modalities is important to review. Given the broad scope of radiation oncology, this article focuses on the 3 most common sites requiring radiation treatment: breast, prostate, and lung cancer.
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http://dx.doi.org/10.1016/j.semradonc.2013.08.004DOI Listing
January 2014

National residency matching program results for radiation oncology: 2012 update.

Int J Radiat Oncol Biol Phys 2013 Jul;86(3):402-4

Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA.

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http://dx.doi.org/10.1016/j.ijrobp.2013.01.018DOI Listing
July 2013

The influence of regional health system characteristics on the surgical management and receipt of post operative radiation therapy for glioblastoma multiforme.

J Neurooncol 2013 May 15;112(3):393-401. Epub 2013 Feb 15.

Yale School of Medicine, New Haven, CT, USA.

Despite a known optimal treatment protocol for the management of glioblastoma multiforme (GBM), many patients fail to receive complete surgical resection or post-operative radiation therapy (PORT). The underlying reasons behind this disparity are unclear. Our study investigates the influence of regional health system resources on the surgical management and PORT receipt in patients with GBM. Surgical intervention, PORT receipt and patient data for patients diagnosed with GBM were obtained from the years 2004 to 2008 from the NCI Surveillance, Epidemiology, and End Results database and combined with the health system data from the Area Resource File. Four logistic models were constructed to test the effect of health system characteristics on surgical treatment choice and PORT receipt among health service areas (HSAs). We found that younger, married patients in HSAs with higher median incomes were significantly more likely to receive both gross total resection (p < 0.001, p < 0.001, p = 0.002) and PORT (p < 0.001, p < 0.001, p = 0.008). The density of radiation oncology equipped hospitals was also a significant predictor of PORT receipt (p = 0.002). Our findings suggest regional variations in of neuro-oncology services and income may have impact on GBM management. Policies aimed at narrowing disparities in treatment may need to focus on addressing regional variations in oncology resources.
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http://dx.doi.org/10.1007/s11060-013-1068-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3641833PMC
May 2013