Journal article
Enhancing PRRT Outcome Prediction in Neuroendocrine Tumors: Aggregated Multi-Lesion PET Radiomics Incorporating Inter-Tumor Heterogeneity
Cancers, Vol.17(23), 3887
12/04/2025
DOI: 10.3390/cancers17233887
PMCID: PMC12690984
PMID: 41375088
Abstract
Simple Summary
This study explored how aggregating radiomic features from multiple PET-identified lesions can predict disease progression and Time to Progression (TTP) in patients with Neuroendocrine Tumors (NETs) treated with [177Lu]Lu-DOTA-TATE. Using different lesion selection and aggregation strategies, we found that selecting the top lesions sorted by descending minimum Standard Uptake Value (SUVmin) improved progression prediction, while aggregating all lesion features or using the top five by descending SUVmean yielded more accurate TTP predictions. The best-performing models were Logistic Regression (LR) for progression and Random Survival Forest (RSF) for TTP, highlighting the value of spatial heterogeneity features. Overall, the findings show that strategic lesion selection and aggregation can enhance predictive accuracy and support more personalized PRRT treatment planning.
Abstract
Introduction: Peptide Receptor Radionuclide Therapy (PRRT) with [177Lu]Lu-DOTA-TATE is effective in treating advanced Neuroendocrine Tumors (NETs), yet predicting individual response in this treatment remains a challenge due to inter-lesion heterogeneity. There is a lack of standardized, effective methods for using multi-lesion radiomics to predict progression and Time to Progression (TTP) in PRRT-treated patients. This study evaluated how aggregating radiomic features from multiple PET-identified lesions can be used to predict disease progression (event [progression and death] vs. event-free) and TTP. Methods: Eighty-one NETs patients with multiple lesions underwent pre-treatment PET/CT imaging. Lesions were segmented and ranked by minimum Standard Uptake Value (SUVmin) (both descending and ascending), SUVmean, SUVmax, and volume (descending). From each sorting, the top one, three, and five lesions were selected. For the selected lesions, radiomic features were extracted (using the Pyradiomics library) and lesion aggregation was performed using stacked vs. statistical methods. Eight classification models along with three feature selection methods were used to predict progression, and five survival models and three feature selection methods were used to predict TTP under a nested cross-validation framework. Results: The overall appraisal showed that sorting lesions based on SUVmin (descending) yields better classification performance in progression prediction. This is in addition to the fact that aggregating features extracted from all the lesions, as well as the top five lesions sorted by SUVmean, lead to the highest overall performance in TTP prediction. The individual appraisal in progression prediction models trained on the single top lesion sorted by SUVmin (descending) showed the highest recall and specificity despite data imbalance. The best-performing model was the Logistic Regression (LR) classifier with Recursive Feature Elimination (RFE) (recall: 0.75, specificity: 0.77). In TTP prediction, the highest concordance index was obtained using a Random Survival Forest (RSF) trained on statistically aggregated features from the top five lesions ranked by SUVmean, selected via Univariate C-Index (UCI) (C-index = 0.68). Across both tasks, features from the Gray Level Size Zone Matrix (GLSZM) family were consistently among the most predictive, highlighting the importance of spatial heterogeneity in treatment response. Conclusions: This study demonstrates that informed lesion selection and tailored aggregation strategies significantly impact the predictive performance of radiomics-based models for progression and TTP prediction in PRRT-treated NET patients. These approaches can potentially enhance model accuracy and better capture tumor heterogeneity, supporting more personalized and practical PRRT implementation.
Details
- Title: Subtitle
- Enhancing PRRT Outcome Prediction in Neuroendocrine Tumors: Aggregated Multi-Lesion PET Radiomics Incorporating Inter-Tumor Heterogeneity
- Creators
- Maziar Sabouri - BC ResearchGhasem Hajianfar - University Hospital of GenevaOmid Gharibi - University of British ColumbiaAlireza Rafiei Sardouei - University of British ColumbiaYusuf Menda - University of IowaAyca Dundar - University of IowaCamila Gadens Zamboni - Division of Nuclear Medicine, Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USASanchay Jain - University of IowaMarc KruzerHabib Zaidi - University of GenevaFereshteh Yousefirizi - BC ResearchArman Rahmim - University of British ColumbiaAhmad Shariftabrizi - Division of Nuclear Medicine, Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA
- Resource Type
- Journal article
- Publication Details
- Cancers, Vol.17(23), 3887
- DOI
- 10.3390/cancers17233887
- PMID
- 41375088
- PMCID
- PMC12690984
- NLM abbreviation
- Cancers (Basel)
- ISSN
- 2072-6694
- eISSN
- 2072-6694
- Publisher
- MDPI
- Grant note
- DH-2025-00119 / Natural Sciences and Engineering Research Council of Canada PJT-173231 / CIHR
- Language
- English
- Date published
- 12/04/2025
- Academic Unit
- Radiology; Radiation Oncology
- Record Identifier
- 9985090599902771
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