CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons

Scavuzzo, Anna; Figueroa-Rodriguez, Pavel; Stefano, Alessandro; Guedulain, Nallely Jimenez; Araiza, Sebastian Muruato; Gomez, Jose de Jesus Cendejas; Compeaán, Alejandro Quiroz; Vargas, Dimas O. Victorio; Jiménez-Ríos, Miguel Ángel

doi:10.3390/jimaging9030071

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…TGCT metastasis commonly occurs in the retroperitoneal lymph nodes located in the abdominal region behind the peritoneum [3]. These are the most frequent sites where TGCTs spread beyond the initial site of origin [4].…”

Section: Introductionmentioning

confidence: 99%

Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection

Scavuzzo,

Pasini,

Crescio

et al. 2023

J. Imaging

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Background: The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. Aim: To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. Methods: Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models’ performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. Result: Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. Conclusions: The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow.

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Section: Introductionmentioning

confidence: 99%

Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection

Scavuzzo,

Pasini,

Crescio

et al. 2023

J. Imaging

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Understanding Sociodemographic Factors among Hispanics Through a Population-Based Study on Testicular Cancer in Mexico

Ríos-Rodríguez,

Montalvo-Casimiro,

Álvarez-López

et al. 2023

J. Racial and Ethnic Health Disparities

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Testicular cancer (TCa) is a rare malignancy affecting young men worldwide. Sociodemographic factors, especially socioeconomic level (SEL) and healthcare access, seem to impact TCa incidence and outcomes, particularly among Hispanic populations. However, limited research has explored these variables in Hispanic groups. This study aimed to investigate sociodemographic and clinical factors in Mexico and their role in health disparities among Hispanic TCa patients. We retrospectively analyzed 244 Mexican TCa cases between 2007 and 2020 of a representative cohort with diverse social backgrounds from a national reference cancer center. Logistic regression identified risk factors for fatality: non-seminoma histology, advanced stage, and lower education levels. Age showed a significant trend as a risk factor. Patient delay and healthcare distance lacked significant associations. Inadequate treatment response and chemotherapy resistance were more likely in advanced stages, while higher education positively impacted treatment response. Cox regression highlighted non-seminoma histology, below-median SEL, higher education, and advanced-stage survival rates. Survival disparities emerged based on tumor histology and patient SEL. This research underscores the importance of comprehensive approaches that integrate sociodemographic, biological, and environmental factors to address health disparities improving outcomes through personalized interventions in Hispanic individuals with TCa.

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Other genitourinary cancers and AI (penile, urethra, and testes)

Ghoreifi,

Sheybaee Moghaddam,

Djaladat

2025

Artificial Intelligence in Urology

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CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons

Cited by 3 publications

References 24 publications

Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection

Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection

Understanding Sociodemographic Factors among Hispanics Through a Population-Based Study on Testicular Cancer in Mexico

Other genitourinary cancers and AI (penile, urethra, and testes)

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