Huo, Yuankai

Foundation models, known as the large-scale, pretrained models capable of generalizing across diverse tasks, have significantly advanced the field of medical image analysis. While most early applications focused on 2D modalities, the unique challenges and opportunities associated with volumetric medical imaging have recently attracted growing interest. This study provides a comprehensive overview of the current landscape of foundation models tailored for volumetric medical image analysis, with a focus on CT, MRI, and PET imaging. We examine key components of these models, including 3D architectures, training strategies, and supported modalities. In addition, we highlight their contribution to major clinical tasks such as classification and prediction, segmentation, image registration, quality enhancement, and visual question answering. Critical challenges of these models, including high computational cost, limited and less diverse 3D datasets, and domain adaptation, are discussed alongside the promising solutions and future research directions. By synthesizing recent advances in volumetric foundation models and outlining key technical and clinical challenges, this review provides a thorough roadmap toward the development of scalable, generalizable, and clinically applicable AI systems for volumetric medical images.

Abstract Purpose: Treatment options are limited beyond JAK inhibitors for patients with primary myelofibrosis (MF) or secondary MF. Preclinical studies have revealed that PI3Kδ inhibition cooperates with ruxolitinib, a JAK1/2 inhibitor, to reduce proliferation and induce apoptosis of JAK2V617F-mutant cell lines. Patients and Methods: In a phase I dose-escalation and -expansion study, we evaluated the safety and efficacy of a selective PI3Kδ inhibitor, umbralisib, in combination with ruxolitinib in patients with MF who had a suboptimal response or lost response to ruxolitinib. Enrolled subjects were required to be on a stable dose of ruxolitinib for ≥8 weeks and continue that MTD at study enrollment. The recommended dose of umbralisib in combination with ruxolitinib was determined using a modified 3+3 dose-escalation design. Safety, pharmacokinetics, and efficacy outcomes were evaluated, and spleen size was measured with a novel automated digital atlas. Results: Thirty-seven patients with MF (median age, 67 years) with prior exposure to ruxolitinib were enrolled. A total of 2 patients treated with 800 mg umbralisib experienced reversible grade 3 asymptomatic pancreatic enzyme elevation, but no dose-limiting toxicities were seen at lower umbralisib doses. Two patients (5%) achieved a durable complete response, and 12 patients (32%) met the International Working Group-Myeloproliferative Neoplasms Research and Treatment response criteria of clinical improvement. With a median follow-up of 50.3 months for censored patients, overall survival was greater than 70% after 3 years of follow-up. Conclusions: Adding umbralisib to ruxolitinib in patients was well tolerated and may resensitize patients with MF to ruxolitinib without unacceptable rates of adverse events seen with earlier generation PI3Kδ inhibitors. Randomized trials testing umbralisib in the treatment of MF should be pursued.