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.
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 pa...
| Publicado en: | European Radiology Vol. 30; no. 4; pp. 2334 - 2346 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142141855&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142141855 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2020 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142141855 142141855 NLM31828413 142141855 10.1007/s00330-019-06495-z NLM31828413 142141855 ppf: 2334 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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. aug: au: Baessler, Bettina Nestler, Tim Pinto dos Santos, Daniel Paffenholz, Pia Zeuch, Vikram Pfister, David Maintz, David Heidenreich, Axel affil: Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany sug: subj: Testicular Neoplasms Lymph Nodes Bioinformatics Neoplasms, Germ Cell and Embryonal Male Orchiectomy Adult Tomography, X-Ray Computed Methods Neoplasms, Germ Cell and Embryonal Pathology Testicular Neoplasms Pathology Lymph Node Excision Young Adult Testicular Neoplasms Therapy Neoplasms, Germ Cell and Embryonal Therapy Reproducibility of Results Retroperitoneal Space Middle Age Lymph Nodes Pathology Neoplasm Metastasis Neoplasm Staging Retrospective Design Adult: 19-44 years Middle Aged: 45-64 years Male ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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