Prediction models for the viability of pulmonary metastatic lesions after chemotherapy in nonseminomatous germ cell tumors
- 8 January 2020
- journal article
- research article
- Published by Wiley in International Journal of Urology
- Vol. 27 (3), 206-212
- https://doi.org/10.1111/iju.14162
Abstract
Objectives To analyze predictors associated with viable cells in pulmonary residual lesions after chemotherapy for metastatic testicular nonseminomatous germ cell tumors and to develop models to prioritize pulmonary resection. Methods Between 2008 and 2017, 40 patients underwent pulmonary metastasectomy after chemotherapy for nonseminomatous germ cell tumors. We evaluated these patients, and 326 pulmonary residual lesions were confirmed using computed tomography and pathological evaluations. Relationships with outcomes were analyzed using logistic regression analyses. Risk prediction models were developed, and predictive probabilities for the risk of viable cells were estimated. Results Histological examinations showed that 73 (22%) pulmonary residual lesions contained viable cells: teratomas, 46 (14%); and cancer cells, 37 (11%). Multivariate analyses showed that the predictors associated with cancer cells in the residual lesions were elevated tumor marker levels, multiregimen chemotherapy, increased tumor size 6 months before surgery and the histological composition of the primary lesion, including yolk sac tumors. Additional predictors associated with teratomas were aspect ratio and histological composition of the primary lesion, including teratomas. Conclusions Intratumoral heterogeneity contributes to nonseminomatous germ cell tumor chemoresistance, and primary lesion site yolk sac tumors and teratomas are associated with greater risks of viable cells. Increased residual lesion size during chemotherapy could also be a predictor. Our simple model can predict the presence of viable cells in residual lesions after chemotherapy, and it might assist in decision‐making and prioritizing pulmonary residual lesion resection.Keywords
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