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  5. Multi Time-Point Radiomics Data for Pathological Complete Response (pCR) Prediction After Neo-Adjuvant Chemoradiation for Locally Advanced Rectal Cancer

Multi Time-Point Radiomics Data for Pathological Complete Response (pCR) Prediction After Neo-Adjuvant Chemoradiation for Locally Advanced Rectal Cancer

M diMayorca1*, Y Zhang2, L Shi3, X Sun4, S Jabbour5, Y Zhang6, N Yue7, K Nie8, (1) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (2) University of California Irvine, Irvine, CA, (3) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (4) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (5) Rutgers Cancer Institute Of New Jersey, New Brunswick, NJ, (6) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (7) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (8) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ

Presentations

(Monday, 7/13/2020) 4:30 PM - 5:30 PM [Eastern Time (GMT-4)]

Room: Track 1

Purpose: To investigate the effectiveness using multi-time point radiomics in predicting a pathological complete response to neoadjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC).


Methods: A total of 95 patients were included, all with pre-treatment (pre-nCRT) and 4 weeks after radiation (mid-nCRT) multi-parametric MRI sets as T1, T2, diffusion weighted imaging (DWI), and dynamic-contrast-enhanced (DCE) MRI. A total of 10940 radiomics features were extracted from these images plus 18 clinical features as TNM staging, CEA level etc. Least absolute shrinkage and selection operator (LASSO) was performed to select key features and build a radiomics signature. Combining clinical risk factors, a radiomics nomogram was constructed. The predictive performance was evaluated by receiver operator characteristic (ROC) curve analysis, and then assessed with respect to its calibration, discrimination and clinical usefulness.


Results: The radiomics signature derived from joint T2-w and DCE-MRI from pre-treatment, delta changes of DWI radiomics, was significantly correlated with pCR status and showed better predictive performance than signatures derived from either time point alone. The radiomics signature showed good discrimination, with areas under the ROC curves (AUCs) of 0.96 and 0.83, as well as good calibration in both training and validation datasets. Decision curve analysis confirmed its clinical usefulness.


Conclusion: Radiomics based on pre-treatment and early follow-up multi-parametric MRI in LARC patients receiving CRT could extract comprehensive quantitative information to predict final pathologic response.

Keywords

MRI, ROC Analysis, Quantitative Imaging

Taxonomy

IM- MRI : Radiomics

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