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Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection


Baessler, Bettina; Nestler, Tim; Pinto dos Santos, Daniel; Paffenholz, Pia; Zeuch, Vikram; Pfister, David; Maintz, David; Heidenreich, Axel (2020). Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection. European Radiology, 30(4):2334-2345.

Abstract

Objectives: To evaluate whether a computed tomography (CT) radiomics-based machine learning classifier can predict histopathology of lymph nodes (LNs) after post-chemotherapy LN dissection (pcRPLND) in patients with metastatic non-seminomatous testicular germ cell tumors (NSTGCTs).
Methods: Eighty patients with retroperitoneal LN metastases and contrast-enhanced CT were included into this retrospective study. Resected LNs were histopathologically classified into "benign" (necrosis/fibrosis) or "malignant" (viable tumor/teratoma). On CT imaging, 204 corresponding LNs were segmented and 97 radiomic features per LN were extracted after standardized image processing. The dataset was split into training, test, and validation sets. After stepwise feature reduction based on reproducibility, variable importance, and correlation analyses, a gradient-boosted tree was trained and tuned on the selected most important features using the training and test datasets. Model validation was performed on the independent validation dataset.
Results: The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a misclassification of 8 of 36 benign LNs as malignant and 4 of 25 malignant LNs as benign (sensitivity 88%, specificity 72%, negative predictive value 88%). In contrast, a model containing only the LN volume resulted in a classification accuracy of 0.68 with 64% sensitivity and 68% specificity.
Conclusions: CT radiomics represents an exciting new tool for improved prediction of the presence of malignant histopathology in retroperitoneal LN metastases from NSTGCTs, aiming at reducing overtreatment in this group of young patients. Thus, the presented approach should be combined with established clinical biomarkers and further validated in larger, prospective clinical trials.
Key points: • Patients with metastatic non-seminomatous testicular germ cell tumors undergoing post-chemotherapy retroperitoneal lymph node dissection of residual lesions show overtreatment in up to 50%. • We assessed whether a CT radiomics-based machine learning classifier can predict histopathology of lymph nodes after post-chemotherapy lymph node dissection. • The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a sensitivity of 88% and a specificity of 78%, thus allowing for prediction of the presence of viable tumor or teratoma in retroperitoneal lymph node metastases.

Abstract

Objectives: To evaluate whether a computed tomography (CT) radiomics-based machine learning classifier can predict histopathology of lymph nodes (LNs) after post-chemotherapy LN dissection (pcRPLND) in patients with metastatic non-seminomatous testicular germ cell tumors (NSTGCTs).
Methods: Eighty patients with retroperitoneal LN metastases and contrast-enhanced CT were included into this retrospective study. Resected LNs were histopathologically classified into "benign" (necrosis/fibrosis) or "malignant" (viable tumor/teratoma). On CT imaging, 204 corresponding LNs were segmented and 97 radiomic features per LN were extracted after standardized image processing. The dataset was split into training, test, and validation sets. After stepwise feature reduction based on reproducibility, variable importance, and correlation analyses, a gradient-boosted tree was trained and tuned on the selected most important features using the training and test datasets. Model validation was performed on the independent validation dataset.
Results: The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a misclassification of 8 of 36 benign LNs as malignant and 4 of 25 malignant LNs as benign (sensitivity 88%, specificity 72%, negative predictive value 88%). In contrast, a model containing only the LN volume resulted in a classification accuracy of 0.68 with 64% sensitivity and 68% specificity.
Conclusions: CT radiomics represents an exciting new tool for improved prediction of the presence of malignant histopathology in retroperitoneal LN metastases from NSTGCTs, aiming at reducing overtreatment in this group of young patients. Thus, the presented approach should be combined with established clinical biomarkers and further validated in larger, prospective clinical trials.
Key points: • Patients with metastatic non-seminomatous testicular germ cell tumors undergoing post-chemotherapy retroperitoneal lymph node dissection of residual lesions show overtreatment in up to 50%. • We assessed whether a CT radiomics-based machine learning classifier can predict histopathology of lymph nodes after post-chemotherapy lymph node dissection. • The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a sensitivity of 88% and a specificity of 78%, thus allowing for prediction of the presence of viable tumor or teratoma in retroperitoneal lymph node metastases.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Diagnostic and Interventional Radiology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Radiology, Nuclear Medicine and Imaging
Uncontrolled Keywords:Radiology Nuclear Medicine and imaging, General Medicine
Language:English
Date:1 April 2020
Deposited On:15 Dec 2020 19:39
Last Modified:16 Dec 2020 21:01
Publisher:Springer
ISSN:0938-7994
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/s00330-019-06495-z

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