Purpose: The objective of our study is to propose fast, cost-effective, convenient, and effective biomarkers using the perfusion parameters from dynamic contrast-enhanced ultrasound (DCE-US) for the evaluation of immune checkpoint inhibitors (ICI) early response. Methods: The retrospective cohort used in this study included 63 patients with metastatic cancer eligible for immunotherapy. DCE-US was performed at baseline, day 8 (D8), and day 21 (D21) after treatment onset. A tumor perfusion curve was modeled on these three dates, and change in the seven perfusion parameters was measured between baseline, D8, and D21. These perfusion parameters were studied to show the impact of their variation on the overall survival (OS). Results: After the removal of missing or suboptimal DCE-US, the Baseline-D8, the Baseline-D21, and the D8-D21 groups included 37, 53, and 33 patients, respectively. A decrease of more than 45% in the area under the perfusion curve (AUC) between baseline and D21 was significantly associated with better OS (p = 0.0114). A decrease of any amount in the AUC between D8 and D21 was also significantly associated with better OS (p = 0.0370). Conclusion: AUC from DCE-US looks to be a promising new biomarker for fast, effective, and convenient immunotherapy response evaluation.
The need for developing new biomarkers is increasing with the emergence of many targeted therapies. In this study, we used artificial intelligence (AI) to develop a multimodal model (PULS-AI) predicting the survival of solid tumor patients treated with antiangiogenic treatments. Our retrospective, multicentric study included 616 patients with 7 different cancer types: renal cell carcinoma, colorectal carcinoma, hepatocellular carcinoma, gastrointestinal carcinoma, melanoma, breast cancer, and sarcoma. A set of 196 patients was left out of the study for validation. Clinical data including patient, treatment, and cancer metadata were collected at baseline for all patients, as well as computed tomography (CT) and ultrasound (US) images. Radiologists annotated all metastases on the CT images and the visible tumor lesion on the US images. AI models were used to extract relevant features from the regions of interest on CT and US images. In addition, handcrafted features related to the tumor burden were extracted from the annotations of all lesions on CT such as the number of lesions and the tumor burden volume per organ (lungs, liver, skull, bone, other). Finally, a Cox regression model was fitted to the set of imaging features and clinical features. The annotation process led to 1147 annotated US images with lesions delineation and 4564 reviewed CTs, of which 989 were selected and fully annotated with a total of 9516 annotated lesions.The developed model reaches an average concordance index of 0.71 (0.67-0.75, 95% CI). Using a risk threshold of 50%, PULS-AI model is able to significantly isolate (log-rank test P-value < 0.001) high-risk patients from low-risk patients (respective median OS of 12 and 32 months) with a hazard ratio of 3.52 (2.35-5.28, 95% CI). The results of this study show that AI algorithms are able to extract relevant information from radiology images and to aggregate data from multiple modalities to build powerful prognostic tools. Such tools may provide assistance to oncology clinicians in therapeutic decision-making. Citation Format: Kathryn Schutte, Fabien Brulport, Sana Harguem-Zayani, Jean-Baptiste Schiratti, Ridouane Ghermi, Paul Jehanno, Alexandre Jaeger, Talal Alamri, Raphael Naccache, Leila Haddag-Miliani, Teresa Orsi, Jean-Philippe Lamarque, Isaline Hoferer, Littisha Lawrance, Baya Benatsou, Imad Bousaid, Mickael Azoulay, Antoine Verdon, François Bidault, Corinne Balleyguier, Victor Aubert, Etienne Bendjebbar, Charles Maussion, Nicolas Loiseau, Benoit Schmauch, Meriem Sefta, Gilles Wainrib, Thomas Clozel, Samy Ammari, Nathalie Lassau. PULS-AI: A multimodal artificial intelligence model to predict survival of solid tumor patients treated with antiangiogenics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1924.
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