Universität zu Köln

Baessler, Bettina ORCID: 0000-0002-3244-3864, Nestler, Tim, dos Santos, Daniel, Paffenholz, Pia, Zeuch, Vikram, Pfister, David, Maintz, David and 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. Eur. Radiol., 30 (4). S. 2334 - 2346. NEW YORK: SPRINGER. ISSN 1432-1084

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Baessler, Bettina UNSPECIFIED orcid.org/0000-0002-3244-3864 UNSPECIFIED Nestler, Tim UNSPECIFIED UNSPECIFIED UNSPECIFIED dos Santos, Daniel UNSPECIFIED UNSPECIFIED UNSPECIFIED Paffenholz, Pia UNSPECIFIED UNSPECIFIED UNSPECIFIED Zeuch, Vikram UNSPECIFIED UNSPECIFIED UNSPECIFIED Pfister, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Maintz, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Heidenreich, Axel UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-339680
DOI:	10.1007/s00330-019-06495-z
Journal or Publication Title:	Eur. Radiol.
Volume:	30
Number:	4
Page Range:	S. 2334 - 2346
Date:	2020
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	1432-1084
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language TEXTURE ANALYSIS; CT TEXTURE; CANCER; CHEMOTHERAPY; PREDICTION; HISTOLOGY; MODEL; MASS Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/33968