Testicular radiomics correlated with pathology at time of post-chemotherapy retroperitoneal lymph node dissection for non-seminomatous germ cell tumor.

Authors

null

Jacob Taylor

UTSW Medical Center, Dallas, TX

Jacob Taylor , Nikit Venishetty , Yin Xi , Jeffrey Howard , Yee Seng Ng , Aditya Bagrodia

Organizations

UTSW Medical Center, Dallas, TX, Texas Tech University Health Sciences Center, El Paso, TX, University of Texas Southwestern Medical Center, Dallas, TX, University of California San Diego Health, La Jolla, CA

Research Funding

No funding received
None.

Background: Testicular germ cell tumors are the most commonly diagnosed malignancy in men aged 20 to 39 years old. Up to a third of patients will have metastatic disease at presentation typically managed with upfront chemotherapy. For many patients with metastatic non-seminomatous germ cell tumor (NSGCT), post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) is performed to further stage and treat residual disease in the retroperitoneum. Although 50% of patients will have viable GCT or teratoma, we do not have accurate tools to predict pre-operatively which patients will have residual disease after chemotherapy. Testicular radiomics is an emerging field that collects complex quantitative tumor imaging data from conventional imaging to aid in clinical decision making. Our aim was to use testicular radiomics data to predict pathology after PC-RPLND. Methods: We extracted radiomics data on 45 patients with metastatic NSGCT undergoing PC-RPLND from 2008-2019. Clinical and pathologic data were collected. Regions of interest (ROI) around metastatic nodes were drawn by a dedicated abdominal radiologist. PyRadiomics, an open-source imaging extraction software, was used to extract first order, shape, and second order statistics from each ROI. Multiple t-tests of testing difference in radiomic features between binary pathology type were performed. P values were adjusted using the BH method to control false discovery rate. Boxplot of features with adjusted p value < 0.05 were shown. Radiomic feature extraction was done in python 3.7 and statistical analyses were done in R 4.2.0. Results: There were 16 (36%) clinical stage II patients and 28 (62%) clinical stage III. 19 (42%) patients had necrosis on PC-RPLND pathology, while 24 (53%) and 2 (4%) patients had teratoma and viable germ cell tumor, respectively. First order statistics mean, median, 90th percentile and root mean squares were significant. Strong correlations were observed between these four features and a lower signal was associated with positive pathology (Table). No significant difference was observed in other first order, shape, or texture features. Conclusions: Testicular radiomics is an emerging tool that has the potential to help predict which patients with metastatic NSGCT are at higher risk of persistent disease after chemotherapy. This study found relatively few first order radiomic data that were correlated with post-operative pathology. Further precision of extraction of the radiomics data may improve clinical decision-making in patients with metastatic NSGCT after chemotherapy prior to RPLND.

Correlation calculator.
First order 90 Percentile First order MeanFirst order Median
First order Mean0.91 (0.84, 0.96)
First order Median0.92 (0.86, 0.96)0.98 (0.97, 0.93)
First order Root Mean Squared0.88 (0.77, 0.99)0.87 (0.76, 0.93)0.88 (0.78, 0.94)

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Abstract Details

Meeting

2023 ASCO Genitourinary Cancers Symposium

Session Type

Poster Session

Session Title

Poster Session C: Renal Cell Cancer; Adrenal, Penile, Urethral and Testicular Cancers

Track

Renal Cell Cancer,Adrenal Cancer,Penile Cancer,Testicular Cancer,Urethral Cancer

Sub Track

Diagnostics and Imaging

Citation

J Clin Oncol 41, 2023 (suppl 6; abstr 413)

DOI

10.1200/JCO.2023.41.6_suppl.413

Abstract #

413

Poster Bd #

L15

Abstract Disclosures

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