Nuria Porta

Patients with multiple myeloma are frequently exposed to prolonged, multiline and multidrug treatments, factors that may substantially influence overall health, physical reserve, and physiological resilience. Imaging of organs that are captured by whole-body scans can be opportunistically interrogated to derive direct, quantitative measures of body composition. Advances and standardization of whole-body magnetic resonance imaging (WBMRI) together with recent improvements in artificial intelligence (AI) methodologies for automated organ and tissue segmentation presents an opportunity to complement accurate disease assessments with body composition metrics. We report, to our knowledge, for the first time, development of an AI-based pipeline to enable automated quantitative image-derived phenotypes in nondiseased tissue from WBMRI in patients with multiple myeloma. We have demonstrated that a deep-learning pipeline can derive body composition metrics from routinely acquired clinical WBMRI. Throughout treatment, significant longitudinal changes were observed (P< .001), characterized by a decrease in abdominal skeletal muscle (ASM) alongside transient increases in abdominal subcutaneous and visceral adipose tissue. We have also shown that greater reserves of ASM (hazard ratio [HR], 0.60; 95% confidence interval [CI], 0.40-0.89) and abdominal subcutaneous adipose tissue (HR, 0.67; 95% CI, 0.46-0.98) at baseline are associated with better progression-free survival. Conversely increases in visceral adipose tissue over time was associated with inferior progression-free survival (HR, 2.89; 95% CI, 1.65-5.09). These findings support opportunistic body composition phenotyping from diagnostic WBMRI as a scalable biomarker for risk stratification (concordance index, 0.725) and mechanistic study. This trial was registered at www.clinicaltrials.gov as NCT02403102.

Objective . Quantitative assessment of treatment response in advanced prostate cancer (APC) with bone metastases remains an unmet clinical need. Whole-body diffusion-weighted MRI (WB-DWI) provides two response biomarkers: total diffusion volume (TDV) and global apparent diffusion coefficient (gADC). However, tracking post-treatment changes of TDV and gADC from manually delineated lesions is cumbersome and increases inter-reader variability. We developed a software to automate this process. Approach . Core technologies include: (i) a weakly-supervised Residual U-Net model generating a skeleton probability map to isolate bone; (ii) a statistical framework for WB-DWI intensity normalisation, obtaining a signal-normalised b = 900 s mm ⁻² (b900) image; and (iii) a shallow convolutional neural network that processes outputs from (i) and (ii) to generate a mask of suspected bone lesions, characterised by higher b900 signal intensity due to restricted water diffusion. This mask is applied to the gADC map to extract TDV and gADC statistics. We tested the tool using expert-defined metastatic bone disease delineations on 66 datasets, assessed repeatability of imaging biomarkers ( N = 10), and compared software-based response assessment with a construct reference standard , defined as multidisciplinary consensus based on ⩾12 months of imaging, clinical, and laboratory follow-up ( N = 118). Main results . Average dice score between manual and automated delineations was 0.6 for lesions within pelvis and spine, with an average surface distance of 2 mm. Relative differences for log-transformed TDV (log-TDV) and median gADC were 8.8% and 5%, respectively. Repeatability analysis showed coefficients of variation of 4.6% for log-TDV and 3.5% for median gADC, with intraclass correlation coefficients of 0.94 or higher. The software achieved 80.5% accuracy, 84.3% sensitivity, and 85.7% specificity in assessing response to treatment. Average computation time was 90 s per scan. Significance . Our software enables reproducible TDV and gADC quantification from WB-DWI scans for monitoring metastatic bone disease response, thus providing potentially useful measurements for clinical decision-making in APC patients.

Quantitative analysis of pre- and postprocedure CT images more reliably identified residual tumor and local tumor progression compared with visual assessment in patients who underwent microwave ablation of colorectal liver metastases.

Supplementary Figure S2. Illustration of the data that is used to derive the ΔR1, HFMRI and HVMRI parameters.

Supplementary Figure S8. Treatment-induced changes in OE-MRI biomarkers relative to baseline (BL).

Supplementary Figure S4. Motion correction effect on ΔR1 time-series.

Supplementary Table S4. Summary of cohort lesion parameter median values at baseline and W2 and W4.

Evaluation of concordance of hypoxia modification in patients with two or more lesions, by assessing serial values of HVMRI at pretreatment and W2 and W4.