Kye Jin Park1, Jae-Lyun Lee2, Shin-Kyo Yoon2, Changhoe Heo3, Bum Woo Park3, Jeong Kon Kim4. 1. Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea. 2. Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 3. Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea. 4. Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea. kim.jeongkon@gmail.com.
Abstract
OBJECTIVES: To evaluate the usefulness of a radiomics-based prediction model for predicting response and survival outcomes of patients with metastatic urothelial carcinoma treated with immunotherapy targeting programmed cell death 1 (PD-1) and its ligand (PD-L1). METHODS: Sixty-two patients who underwent immunotherapy were divided into training (n = 41) and validation sets (n = 21). A total of 224 measurable lesions were identified on contrast-enhanced CT. A radiomics signature was constructed with features selected using a least absolute shrinkage and selection operator algorithm in the training set. A radiomics-based model was built based on a radiomics signature consisting of five reliable RFs and the presence of visceral organ involvement using multivariate logistic regression. According to a cutoff determined on the training set, patients in the validation set were assigned to either high- or low-risk groups. Kaplan-Meier analysis was performed to compare progression-free and overall survival between high- and low-risk groups. RESULTS: For predicting objective response and disease control, the areas under the receiver operating characteristic curves of the radiomics-based model were 0.87 (95% CI, 0.65-0.97) and 0.88 (95% CI, 0.67-0.98) for the validation set, providing larger net benefit determined by decision curve analysis than without radiomics-based model. The high-risk group in the validation set showed shorter progression-free and overall survival than the low-risk group (log-rank p = 0.044 and p = 0.035). CONCLUSIONS: The radiomics-based model may predict the response and survival outcome in patients treated with PD-1/PD-L1 immunotherapy for metastatic urothelial carcinoma. This approach may provide important and decision tool for planning immunotherapy. KEY POINTS: • A radiomics-based model was built based on radiomics features and the presence of visceral organ involvement for prediction of outcomes in metastatic urothelial carcinoma treated with immunotherapy. • This prediction model demonstrated good prediction of treatment response and higher net benefit than no model in the independent validation set. • This radiomics-based model demonstrated significant associations with progression-free and overall survival between low-risk and high-risk groups.
OBJECTIVES: To evaluate the usefulness of a radiomics-based prediction model for predicting response and survival outcomes of patients with metastatic urothelial carcinoma treated with immunotherapy targeting programmed cell death 1 (PD-1) and its ligand (PD-L1). METHODS: Sixty-two patients who underwent immunotherapy were divided into training (n = 41) and validation sets (n = 21). A total of 224 measurable lesions were identified on contrast-enhanced CT. A radiomics signature was constructed with features selected using a least absolute shrinkage and selection operator algorithm in the training set. A radiomics-based model was built based on a radiomics signature consisting of five reliable RFs and the presence of visceral organ involvement using multivariate logistic regression. According to a cutoff determined on the training set, patients in the validation set were assigned to either high- or low-risk groups. Kaplan-Meier analysis was performed to compare progression-free and overall survival between high- and low-risk groups. RESULTS: For predicting objective response and disease control, the areas under the receiver operating characteristic curves of the radiomics-based model were 0.87 (95% CI, 0.65-0.97) and 0.88 (95% CI, 0.67-0.98) for the validation set, providing larger net benefit determined by decision curve analysis than without radiomics-based model. The high-risk group in the validation set showed shorter progression-free and overall survival than the low-risk group (log-rank p = 0.044 and p = 0.035). CONCLUSIONS: The radiomics-based model may predict the response and survival outcome in patients treated with PD-1/PD-L1 immunotherapy for metastatic urothelial carcinoma. This approach may provide important and decision tool for planning immunotherapy. KEY POINTS: • A radiomics-based model was built based on radiomics features and the presence of visceral organ involvement for prediction of outcomes in metastatic urothelial carcinoma treated with immunotherapy. • This prediction model demonstrated good prediction of treatment response and higher net benefit than no model in the independent validation set. • This radiomics-based model demonstrated significant associations with progression-free and overall survival between low-risk and high-risk groups.
Authors: Roger Sun; Théophraste Henry; Adrien Laville; Alexandre Carré; Anthony Hamaoui; Sophie Bockel; Ines Chaffai; Antonin Levy; Cyrus Chargari; Charlotte Robert; Eric Deutsch Journal: J Immunother Cancer Date: 2022-07 Impact factor: 12.469
Authors: Jarey H Wang; Kareem A Wahid; Lisanne V van Dijk; Keyvan Farahani; Reid F Thompson; Clifton David Fuller Journal: Clin Transl Radiat Oncol Date: 2021-04-07