Wenzheng Li

Hepatocellular carcinoma (HCC) remains a leading cause of cancer‐related mortality worldwide, with high recurrence rates following curative resection. Accurate non‐invasive assessment of tumor aggressiveness and recurrence risk is critical for surgical decision‐making and postoperative surveillance, yet remains challenging with conventional imaging modalities. Here, we developed a CT lesion foundation model (LFM), trained via self‐supervised learning on 32,120 generic lesion and 6,387 HCC‐specific CT patches to capture transferable intra‐lesional imaging phenotypes. Across one internal and two external validation cohorts, the sequentially pretrained LFM (LFM‐Seq) achieved area under the curve values of 0.916, 0.846, and 0.920 for microvascular invasion (MVI) assessment, and 0.833, 0.885 and 0.795 for pathological grade assessment. The model remained robust under scenarios of limited training data and tumor localization perturbations. Furthermore, LFM‐Seq‐derived risk scores stratified recurrence‐free survival (RFS), yielding an externally validated C‐index of 0.701, and remained independently associated with RFS after adjusting for clinicopathological factors. Exploratory biological interpretability analyses showed that LFM‐Seq‐derived risk phenotypes were associated with suppressed lymphocyte‐related immune programs. These results suggest that CT lesion foundation models can support non‐invasive and robust characterization of HCC aggressiveness, recurrence risk stratification and discovery of interpretable prognostic imaging biomarkers.

Background: Intratumoral fibrosis influences treatment efficacy and prognosis of rectal cancer (RC). The roles of MR elastography (MRE) and Virtual MRE (VMRE) remain unclear in assessing it in RC. Purpose: To evaluate MRE for assessing intratumoral fibrosis in RC, identify optimal b ‐values for VMRE, and compare their diagnostic performance. Study Type: Prospective cohort. Population: A total of 71 participants with pathologically confirmed RC (47 men; mean age, 62 ± 10 years). Field Strength/Sequences: 3T, T2‐weighted turbo‐spin echo, contrast‐enhanced T1‐weighted gradient‐recalled echo, Intravoxel incoherent motion (IVIM) ( b = 0, 100, 200, 1000, 1500 s/mm ² ), and spin‐echo MRE. Assessment: All participants were divided at a 7:3 ratio: subgroup 1 ( N = 49) determined the optimal b ‐value combination and established the empirical relationship between shifted apparent diffusion coefficient (sADC) and VMRE (V c , V φ ) based on IVIM, with MRE ( c , φ ) as reference; subgroup 2 ( N = 22) validated this relationship. MRE and VMRE evaluated intratumoral fibrosis in 43 surgically diagnosed participants. Statistical Tests: Receiver operating characteristic (ROC), Pearson correlation, DeLong test. Bonferroni‐adjusted p < 0.05 indicated statistical significance. Results: The optimal combination of b ‐values was 0 and 1500 s/mm ² ( r _c = −0.808, r _φ = −0.644) for VMRE in subgroup 1. In 43 surgically diagnosed participants, area under curves (AUCs) of c and φ for assessing intratumoral fibrosis were 0.779 and 0.684, respectively (Adjusted p = 0.828). AUCs of V c and V φ converted from sADC _0–1500 values were 0.792 and 0.784, respectively (Adjusted p > 0.999). No significant differences in AUCs were observed among all parameters (Adjusted p : 0.384 to > 0.999). Sensitivity and specificity ranges of MRE and VMRE were 85.7%–90.5% and 54.5%–68.2%, respectively. Data Conclusions: MRE was used to evaluate intratumoral fibrosis in RC. The optimal b ‐values combination for VMRE in RC was 0 s/mm ² and 1500 s/mm ² . No significant difference was observed for assessing intratumoral fibrosis between MRE and VMRE. Evidence Level: 2. Technical Efficacy: Stage 2.

Background: Integrating vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI) (VM hereafter) is potentially useful in risk stratification of hepatocellular carcinoma (HCC). However, noninvasive assessment methods for VM are lacking.Purpose: To investigate the diagnostic performance of tomoelastography in assessing the VM status in HCC.Study Type: Retrospective.Population: One hundred sixty‐eight patients with surgically confirmed HCC consisting of 115 training and 53 validation cohorts, divided into negative‐VM and positive‐VM groups with mild or severe‐VMs. Of them, 127 patients completed the follow‐up (median: 26.1 months).Field Strength/Sequence: 3D multifrequency tomoelastography with a single‐shot spin‐echo echo‐planar imaging sequence, and liver MRI including T1‐weighted in‐phase and opposed‐phase gradient echo (GRE), T2‐weighted turbo spin echo, diffusion‐weighted imaging and dynamic contrast‐enhanced T1‐weighted GRE sequences at 3.0 T.Assessment: Shear wave speed (c) and phase angle of the shear modulus (φ) were calculated on tomoelastograms. Imaging features were visually analyzed and clinical features were collected. Conventional models used clinical and imaging features while nomograms combined tomoelastography, clinical and imaging features.Statistical Tests: Univariable and multivariable logistic regression analyses, nomogram, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan–Meier analysis and log‐rank test. P < 0.05 was considered statistically significant.Results: Tumor‐to‐liver parenchyma ratio of c (cr) and tumor c were independent risk factors for positive‐VM and severe‐VM, respectively. In validation cohort, the nomograms including cr and tumor c performed significantly better than the conventional models for diagnosing positive‐VM (0.84 [95% CI: 0.72–0.93] vs. 0.77 [95% CI: 0.64–0.88]) and severe‐VM (0.86 [95% CI: 0.68–0.96] vs. 0.75 [95% CI: 0.55–0.89]). Patients with estimated positive‐VM (9.3 months)/severe‐VM (9.2 months) based on nomograms had shorter median recurrence‐free survival than those with estimated negative‐VM (>20.0 months)/mild‐VM (18.0 months) in validation cohort.Data Conclusion: Tomoelastography based‐nomograms showed good performance for noninvasively assessing VM status in patients with HCC.Evidence Level: 3Technical Efficacy: Stage 2