Machine learning model integrating CT radiomics and circulating microRNAs to predict residual disease histology in metastatic non-seminoma testicular cancer (mNSTC).
Authors: Ozgun Guliz , Abdalvand Neda , Ozcan Gizem , Nip Ka Mun , Khazamipour Nastaran , Rahmim Arman , Bell Robert H , Soleimani Maryam , Maurice-Dror Corinne , Chi Kim N. , Eigl Bernhard J. , Nichols Craig R. , Kollmannsberger Christian K. , Yuan Ren , Nappi Lucia 
Published: February 2025

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Background:
The primary treatment of most mNSTC is chemotherapy followed by surgery if the residual disease (RD) is >1 cm. However, conventional imaging lacks the specificity to characterize the tissue, often leading to overtreatment. This study hypothesizes that integrating CT-driven radiomics features with plasma miR371 and miR375 will enhance the predictive accuracy of Machine Learning (ML) models to predict teratoma, viable germ cell (vGCT) and fibrosis/necrosis (F/N) in mNSTC patients with RD.
Methods:
111 lesions from52 patients, including residual teratoma (n=57), F/N (n=33), vGCT (n=10), and additional seminoma (n=11) for training purposes were included, split into training (N=78) and test cohorts (N=33). Lesions were lymph nodes (n=87), lung (n=21), and brain (n=3) with a median size of 1.6 cm (Q1-Q3 interval=1.2-2.73 cm). 3D Slicer version 5.6.1 was used to segment the RD > 1 cm (short axis) and extract radiomics features. Plasma miRNA levels before resection were measured by RT-PCR. Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and CatBoost (CB) ML models were evaluated to define the operating characteristics of radiomics alone (R-only) and in combination with miR371 (371) and/or miR375 (375) levels in predicting teratoma, vGCT and F/N.
Results:
For predicting teratoma, the best models were RF (R+375 and R+371+375), CB (R+371+375), and GB (R+371 and R+371+375). While adding miR371 or miR375 to R-only slightly improved AUC across models, the best results were achieved with the R+375+371 dataset. CB achieved AUCs ranging from 0.94 to 0.97 in training and 0.81 to 0.93 in test sets, with its highest AUC of 0.93 (95% CI: 0.78-0.97) on the R+375+371 dataset to differentiate all three classes. Similarly, GB demonstrated strong performance, achieving its highest AUC of 0.93 (95% CI: 0.79-0.96) on the R+375+371 dataset (Table).
Conclusions:
Integration of plasma miR371, miR375 and radiomics improved accuracy of predicting histologies across all ML models. These methods could be used to characterize the histology of RD in mNSTC patients to better inform treatment decisions. Further refinement, including incorporation of histological findings of the primary tumor, will be reported.

AUC values of different ML algorithms on training and test sets.

TRAINING SET
TEST SET

Model ±SD

R

R+375

R+371

R+375+371

Model
(95% CI)

R

R+375

R+371

R+375+371

RF
0.93±0.05
0.95±0.04
0.95±0.03
0.96±0.04

RF

0.8(0.59-0.89)
0.85(0.72-0.93)
0.87(0.76-0.95)
0.91(0.78-0.95)

SVM
0.84±0.06
0.84±0.09
0.89±0.11
0.89±0.09

SVM

0.72(0.54-0.80)
0.74(0.56-0.82)
0.83(0.69-0.92)
0.84(0.76-0.94)

GB
0.94±0.04
0.91±0.08
0.95±0.05
0.97±0.03

GB

0.84(0.61-0.96)
0.89(0.77-0.97)
0.89(0.79-0.96)
0.93(0.79-0.96)

CB
0.95±0.03
0.94±0.03
0.94±0.04
0.97±0.03

CB

0.81(0.6-0.93)
0.86(0.73-0.94)
0.89(0.78-0.97)
0.93(0.78-0.97)