Publications by authors named "V Madekivi"

4 Publications

  • Page 1 of 1

Characteristics of clinically node negative breast cancer patients needing preoperative MRI.

Surg Oncol 2021 Mar 31;38:101552. Epub 2021 Mar 31.

Department of Oncology, Turku University Hospital, Finland; University of Turku, Turku, Finland; Finnish Nuclear and Radiation Safety, Helsinki, Finland.

Background: International guidelines do not recommend magnetic resonance imaging (MRI) for all breast cancer patients at primary diagnostics. This study aimed to understand which patient or tumor characteristics are associated with the use of MRI. The role of MRI among other preoperative imaging methods in clinically node negative breast cancer was studied.

Material And Methods: Patient and tumor characteristics were analyzed in association with the use of MRI by multivariable logistic regression analysis in 461 patients. Primary tumor size was compared between MRI, mammography (MGR), ultrasound (US) and histopathology by Spearman correlation. The delays in surgery and diagnosis were analyzed among patients with or without MRI, and axillary reoperations were evaluated.

Results: Age (p < 0.0001), primary operation method (p < 0.0001), tumor histology (p < 0.0001) and HER2 status (p = 0.0064) were associated with the use of MRI. Spearman correlations between tumor size in histopathology and the difference in tumor size between histopathology and imaging methods were 0.52 in MGR, 0.66 in US and 0.36 in MRI (p < 0.0001 for all). A seven-day delay in surgical treatment was observed among patients with MRI compared to patients without MRI (p < 0.0001). Axillary reoperation rates were similar in patients with or without MRI (p = 0.57).

Conclusion: Patient selection through prearranged characterization is important in deciding on optimal candidates for preoperative MRI among breast cancer patients. MRI causes moderate delays in primary breast cancer surgery. Preoperative MRI is useful in the evaluation of tumor size but might be insufficient in detecting lymph node metastases.
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http://dx.doi.org/10.1016/j.suronc.2021.101552DOI Listing
March 2021

Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

Oncology 2021 Mar 10:1-5. Epub 2021 Mar 10.

Department of Oncology, University of Turku and Turku University Hospital, Turku, Finland.

Background: Nomograms can help in estimating the nodal status among clinically node-negative patients. Yet their validity in external cohorts over time is unknown. If the nodal stage can be estimated preoperatively, the need for axillary dissection can be decided.

Objectives: The aim of this study was to validate three existing nomograms predicting 4 or more axillary lymph node metastases.

Method: The risk for ≥4 lymph node metastases was calculated for n = 529 eligible breast cancer patients using the nomograms of Chagpar et al. [Ann Surg Oncol. 2007;14:670-7], Katz et al. [J Clin Oncol. 2008;26(13):2093-8], and Meretoja et al. [Breast Cancer Res Treat. 2013;138(3):817-27]. Discrimination and calibration were calculated for each nomogram to determine their validity.

Results: In this cohort, the AUC values for the Chagpar, Katz, and Meretoja models were 0.79 (95% CI 0.74-0.83), 0.87 (95% CI 0.83-0.91), and 0.82 (95% CI 0.76-0.86), respectively, showing good discrimination between patients with and without high nodal burdens.

Conclusion: This study presents support for the use of older breast cancer nomograms and confirms their current validity in an external population.
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http://dx.doi.org/10.1159/000514616DOI Listing
March 2021

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Acta Oncol 2020 Jun 9;59(6):689-695. Epub 2020 Mar 9.

Department of Oncology, Turku University Hospital, Turku, Finland.

The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-making process between axillary treatment options. The aim of this study was to create a new model to predict the nodal stage N2-3 after a positive SLNB using machine learning methods that are rarely seen in nomogram development. Primary breast cancer patients who underwent SLNB and axillary lymph node dissection (ALND) between 2012 and 2017 formed cohorts for nomogram development (training cohort,  = 460) and for nomogram validation (validation cohort,  = 70). A machine learning method known as the gradient boosted trees model (XGBoost) was used to determine the variables associated with nodal stage N2-3 and to create a predictive model. Multivariate logistic regression analysis was used for comparison. The best combination of variables associated with nodal stage N2-3 in XGBoost modeling included tumor size, histological type, multifocality, lymphovascular invasion, percentage of ER positive cells, number of positive sentinel lymph nodes (SLN) and number of positive SLNs multiplied by tumor size. Indicating discrimination, AUC values for the training cohort and the validation cohort were 0.80 (95%CI 0.71-0.89) and 0.80 (95%CI 0.65-0.92) in the XGBoost model and 0.85 (95%CI 0.77-0.93) and 0.75 (95%CI 0.58-0.89) in the logistic regression model, respectively. This machine learning model was able to maintain its discrimination in the validation cohort better than the logistic regression model. This indicates advantages in employing modern artificial intelligence techniques into nomogram development. The nomogram could be used to help identify nodal stage N2-3 in early breast cancer and to select appropriate treatments for patients.
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http://dx.doi.org/10.1080/0284186X.2020.1736332DOI Listing
June 2020

The Sentinel Node with Isolated Breast Tumor Cells or Micrometastases. Benefits and Risks of Axillary Dissection.

Anticancer Res 2017 07;37(7):3757-3762

Department of Oncology and Radiotherapy, Turku University Hospital, University of Turku, Turku, Finland

Background/aim: Sentinel lymph node (SLN) biopsy has become the standard procedure to identify metastases in axillary nodes in breast cancer. Even after careful SLN examination additional micrometastases and isolated tumor cells (ITCs) are sometimes found, resulting in a need for delayed axillary lymph node dissection (ALND). This study was undertaken to assess prognostic factors identifying additional axillary lymph node (ALN) metastases at delayed ALND.

Patients And Methods: To define the impact of late ALND regarding their outcome, 162 breast cancer patients with 169 operated breasts treated between 2010 and 2012 were evaluated, with follow-up through 2016. Data were collected on the patients, histology and biologic profile of the cancer, lymph node involvement, recurrence of breast cancer and adverse effects of ALND.

Results: With thorough examination and immunohistochemical stainings twenty-nine of 168 SLN biopsies (28 patients, 17% of the patients) showed micrometastases or ITC, and a full ALND was performed at a later time. During these ALNDs 13 to 31 lymph nodes were removed. Additional ALN metastases were found in three (10%) patients. Two (7%) of the 28 patients with triple-negative cancer deceased of metastatic breast cancer. Three patients (11%) reported adverse effects of ALND requiring physiotherapy due to pain, stiffness, swelling or arm oedema. Tumor factors such as molecular subtype (p=0.002), tumor size (p=0.004), and proliferation index (Ki-67) (p=0.003) correlated with higher numbers of ALN metastases.

Conclusion: Since most patients with micrometastases found in the primary operation showed no additional positive lymph nodes, completion ALND may not be required in patients with micrometastases or ITCs in the SLN. In our study, the predictive factors for additional ALN metastases were tumur size, molecular subtype and proliferation index. It is conceivable that the features of the primary tumor, rather than the amount of cancer cells in the SLN, might serve to identify patients in whom ALDN can be avoided.
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http://dx.doi.org/10.21873/anticanres.11750DOI Listing
July 2017