Diffusion-based virtual MR elastography for predicting recurrence of solitary hepatocellular carcinoma after hepatectomy.

BACKGROUND: To explore the capability of diffusion-based virtual MR elastography (vMRE) in the preoperative prediction of recurrence in hepatocellular carcinoma (HCC) and to investigate the underlying relevant histopathological characteristics. METHODS: Between August 2015 and December 2016, patients underwent preoperative MRI examination with a dedicated DWI sequence (b-values: 200,1500 s/mm2) were recruited. The ADC values and diffusion-based virtual shear modulus (µdiff) of HCCs were calculated and MR morphological features were also analyzed. The Cox proportional hazards model was used to identify the risk factors associated with tumor recurrence. A preoperative radiologic model and postoperative model including pathological features were built to predict tumor recurrence after hepatectomy. RESULTS: A total of 87 patients with solitary surgically confirmed HCCs were included in this study. Thirty-five patients (40.2%) were found to have tumor recurrence after hepatectomy. The preoperative model included higher µdiff and corona enhancement, while the postoperative model included higher µdiff, microvascular invasion, and histologic tumor grade. These factors were identified as significant prognostic factors for recurrence-free survival (RFS) (all p < 0.05). The HCC patients with µdiff values > 2.325 kPa showed poorer 5-year RFS after hepatectomy than patients with µdiff values ≤ 2.325 kPa (p < 0.001). Moreover, the higher µdiff values was correlated with the expression of CK19 (3.95 ± 2.37 vs. 3.15 ± 1.77, p = 0.017) and high Ki-67 labeling index (4.22 ± 1.63 vs. 2.72 ± 2.12, p = 0.001). CONCLUSIONS: The µdiff values related to the expression of CK19 and Ki-67 labeling index potentially predict RFS after hepatectomy in HCC patients.

Association of magnetic resonance imaging-derived sarcopenia with outcomes of patients with hepatocellular carcinoma after hepatectomy.

PURPOSE: To evaluate whether sarcopenia, diagnosed by magnetic resonance imaging (MRI) protocol, constitutes a prognosis-associated risk factor in patients with hepatocellular carcinoma (HCC) after hepatectomy. METHODS: One hundred and ninety-three patients who underwent hepatectomy for HCC were retrospectively enrolled. The areas of the total skeletal muscle (SM) and psoas muscle (PM) were evaluated at the third lumbar vertebra in the preoperative MR images, and divided by the square of height in order to obtain the skeletal muscle index (SMI) and psoas muscle mass index (PMI). Sarcopenia was diagnosed respectively on the definitions based on the SMI or PMI. The potential of muscle-defined sarcopenia as a prognostic factor for overall survival (OS) and recurrence-free survival (RFS) was investigated in these patients. RESULTS: The areas of SM and PM, and SMI and PMI were significantly higher in the men than in the women (all p < 0.05). Notably, SMI-defined sarcopenia displayed a significant sex difference (p = 0.003), while PMI-defined sarcopenia did not (p = 0.370). Through univariate and multivariate analyses, PMI-defined sarcopenia remained an independent predictor for OS and RFS (HR = 3.486, 95% CI: 1.700-7.145, p = 0.001 and HR = 1.993, 95% CI: 1.246-3.186, p = 0.004), even after adjusting for other clinical variables. Moreover, Kaplan-Meier analysis demonstrated significantly poorer OS and RFS for patients with sarcopenia defined by using PMI, but not SMI, compared to those without sarcopenia (p < 0.001 and p = 0.006, respectively). CONCLUSION: MRI-derived, sarcopenia defined by using PMI, not SMI, may serve as a significant risk factor for RFS and OS in patients with HCC after hepatectomy.

Esophageal cancer detection via non-contrast CT and deep learning.

Background: Esophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images. Methods: In this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted. Results: In this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong's test). Conclusion: The DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.

A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation.

BACKGROUND & AIMS: We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA. METHODS: A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180). RESULTS: Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence. CONCLUSIONS: The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.

Hypoxia-activated cascade nanovaccine for synergistic chemoembolization-immune therapy of hepatocellular carcinoma.

In this work, a promising treatment strategy for triggering robust antitumor immune responses in transarterial chemoembolization of hepatocellular carcinoma (HCC) is presented. The zeolitic imidazolate framework nanoparticles loaded with hypoxia-activated prodrug tirapazamine and immune adjuvant resiquimod facilitated in situ generation of nanovaccine via a facile approach. The nanovaccine can strengthen the ability of killing the liver cancer cells under hypoxic environment, while was capable of improving immunogenic tumor microenvironment and triggering strong antitumor immune responses by increasing the primary and distant intratumoral infiltration of immune cells such as cytotoxic T cells. Moreover, a porous microcarrier, approved by FDA as pharmaceutical excipient, was designed to achieve safe and effective delivery of the nanovaccine via transarterial therapy in rabbit orthotopic VX2 liver cancer model. The microcarrier exhibited the characteristics of excellent drug loading and occlusion of peripheral artery. The collaborative delivery of the microcarrier and nanovaccine demonstrated an exciting inhibitory effect on solid tumors and tumor metastases, which provided a great potential as novel combination therapy for HCC interventional therapy.

Should the Baseline MRI Staging Criteria Differentiate Between Mucinous and Classical Rectal Adenocarcinoma?

RATIONALE AND OBJECTIVES: To compare baseline MR imaging features for pre-treatment staging between rectal mucinous adenocarcinoma (RMAC) and rectal classical adenocarcinoma (RCAC), and to investigate whether the subtype of mucinous carcinoma influences MRI evaluation criteria and high-risk tumors identifying. METHODS: A total of 306 patients who underwent surgical rectal cancer resection were retrospectively reviewed in the study. MR imaging parameters of the primary tumor and lymph nodes (LNs) were compared between two subtypes. Logistic regression and receiver operating characteristic analyses were performed to test significant associations between LN imaging parameters and malignant LN status in RMAC and RCAC, respectively. RESULTS: The length of mucinous tumors was larger than RCAC tumors in pT3 and pT4 stage. For pN0 patients, the long and short diameters of the largest LN on MRI were more likely to be larger in RCAC than RMAC. For pN+ patients, the proportion of LNs exhibiting internal heterogeneity in RMAC was obviously greater than that in RCAC. The best cut-off value of the largest short diameter of malignant LNs was 6.05 mm for RMAC and 8.05 mm for RCAC. And the highest AUC for predicting LNs metastases based on the largest short diameter was 0.794 for RMAC using 6 mm size cut-off, and 0.667 for RCAC using 8 mm cut-off. CONCLUSION: The imaging features that were associated with LN metastases were different between RMAC and RCAC, and different size criteria of LNs was suggested to distinguish high-risk tumors. Clinicians should stay vigilant of LN status and take histologic subtypes into consideration before assigning clinical strategies.

The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023.

The 2023 update of the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Gastric Cancer focuses on standardizing cancer diagnosis and treatment in China, reflecting the latest advancements in evidence-based medicine, healthcare resource availability, and precision medicine. These updates address the differences in epidemiological characteristics, clinicopathological features, tumor biology, treatment patterns, and drug selections between Eastern and Western gastric cancer patients. Key revisions include a structured template for imaging diagnosis reports, updated standards for molecular marker testing in pathological diagnosis, and an elevated recommendation for neoadjuvant chemotherapy in stage III gastric cancer. For advanced metastatic gastric cancer, the guidelines introduce new recommendations for immunotherapy, anti-angiogenic therapy and targeted drugs, along with updated management strategies for human epidermal growth factor receptor 2 (HER2)-positive and deficient DNA mismatch repair (dMMR)/microsatellite instability-high (MSI-H) patients. Additionally, the guidelines offer detailed screening recommendations for hereditary gastric cancer and an appendix listing drug treatment regimens for various stages of gastric cancer. The 2023 CSCO Clinical Guidelines for Gastric Cancer updates are based on both Chinese and international clinical research and expert consensus to enhance their applicability and relevance in clinical practice, particularly in the heterogeneous healthcare landscape of China, while maintaining a commitment to scientific rigor, impartiality, and timely revisions.

The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma.

RATIONALE AND OBJECTIVES: To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA). MATERIALS AND METHODS: Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared. RESULTS: Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001). CONCLUSION: The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.

The largest lymph node defined response to neoadjuvant chemotherapy can predict long-term prognosis in locally advanced gastric cancer.

BACKGROUND: The assessment of tumor response to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC) remain challenging. We aimed to explore the potential role of peri-NACT change of the largest lymph node (LN) and primary tumor (P-T) in the prediction of tumor response and patient overall survival (OS) in LAGC. METHODS: A cohort of LAGC patients who underwent NACT followed by radical surgery from a prospective clinical trial were retrospectively analyzed. The percentage change of the largest LN and P-T from initial to post-NACT Computed Tomography (CT) were measured. Tumor response was defined by the change of LN (LN-response) and P-T (P-T-response), respectively. A multivariate Cox model was constructed to examine if P-T- and LN-determined response had significant predictive ability for OS when adjusting with other possible prognostic factors. RESULTS: Of the 41 patients, 28 (68.3%) was defined as LN-responders to NACT, and 17 (41.5%) patients was defined as P-T-responders. When the cohort was stratified by LN response standard, LN-responders showed a significant longer median OS than LN-nonresponders (p = 0.031, 20.6 vs 16.6 months). When stratified by primary tumor response, no significant difference in OS was observed between P-T-responders and P-T-nonresponders (p = 0.377, 18.5 vs 19.0 months). In the multivariate analysis, number of positive LN (p = 0.004, hazard ratio [HR] = 1.284), recurrence (p = 0.024, HR =3556), LN shrinkage (p = 0.022, HR = 0.930) and LN-response (p = 0.033, HR = 0.008) were observed with independent association with OS. CONCLUSIONS: Peri-NACT change of the largest LN could reflect tumor response to NACT, and LN-defined response was useful in predicting the long-term prognosis (OS) of LAGC patients who underwent NACT followed by radical surgery.

Diagnostic performance of gadoxetic acid-enhanced abbreviated magnetic resonance imaging protocol in small hepatocellular carcinoma (≤2 cm) in high-risk patients.

BACKGROUND: Biannual Ultrasound showed insufficient sensitivity in detecting small or early-stage hepatocellular carcinoma (HCC). Abbreviated magnetic resonance imaging (A-MRI) protocols with fewer sequences demonstrated higher HCC detection sensitivity than ultrasound with acceptable cost and examination time. PURPOSE: To compare the diagnostic performance of gadoxetic acid-enhanced A-MRI with a full sequence MRI (F-MRI) protocol for small HCC (≤2 cm) in cirrhotic or hepatitis B virus-infected high-risk patients. MATERIAL AND METHODS: Two hundred and four consecutive patients with 166 pathologically confirmed small HCC who underwent preoperative gadoxetic acid-enhanced MRI were retrospectively included. A-MRI set comprised T1-weighted hepatobiliary phase imaging, T2-weighted imaging, diffusion-weighted imaging and apparent diffusion coefficient mapping. Two independent radiologists blinded to clinical data assessed the A-MRI set and F-MRI set. Per-patient HCC and per-lesion HCC diagnostic performance were compared. RESULTS: Per-patient HCC detection sensitivity of A-MRI set was 93.8% and 91.2% for observer 1 and observer 2, and, for the F-MRI set, the per-patient HCC detection sensitivity was 96.6% and 95.2%, respectively. There was no significant difference in per-patient sensitivity, specificity and per-lesion HCC detection sensitivity between the two imaging sets for both readers. (P = 0.06-0.25) The A-MRI set showed higher sensitivity on HCC without arterial phase hyperenhancement, and the F-MRI set demonstrated with higher sensitivity on HCC with arterial phase hyperenhancement (P < 0.05). CONCLUSION: A-MRI using diagnostic criteria including hypointensity on hepatobiliary phase plus mild to moderate hyperintensity on T2-weighted imaging or restricted diffusion demonstrated comparable sensitivity and specificity for small HCC compared to the F-MRI protocol in high-risk patients.

A novel role of the splenic volume in Crohn's disease: evaluating the efficacy of infliximab.

Background: A number of patients with Crohn's disease (CD) suffer from loss of response to infliximab (IFX) therapy. Splenic volume is reported to be enlarged in patients with CD compared to normal individuals. The association between splenic volume and IFX efficacy in CD remains unclear. Methods: We performed a retrospective study of patients with CD who received regular IFX treatment at Zhongshan Hospital, Fudan University, between August 2015 and December 2021. We collected baseline characteristics and clinical features from medical records in the CD database of Zhongshan Hospital. We accurately measured the splenic volume using semi-auto spleen segmentation software, followed by the analysis of splenic volume and IFX efficacy. Results: We included 49 patients with CD receiving IFX treatment, of whom 41 responded to IFX and 8 failed to respond to IFX. Splenic volume, as well as volume adjusted for body mass index (SV/BMI) and body weight (SV/W), was significantly decreased after IFX treatment in responders but increased in non-responders compared to the volume before the treatment. Accordingly, the levels of leukocyte count, platelet count, C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were decreased after IFX treatment in responders. Contrarily, the levels of hemoglobin, albumin, and tumor necrosis factor (TNF)-α were elevated in responders. Moreover, both CRP and TNF-α levels were significantly positively correlated with SV/BMI in all patients. Conclusion: Splenic volume, especially SV/BMI and SV/W, was reduced after IFX treatment in CD patients responsive to IFX. SV/BMI was positively correlated with disease activity. Splenic volume is a promising indicator to evaluate IFX efficacy in CD.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.

Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. Methods: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.

Insulin Glargine is More Suitable Than Exenatide in Preventing Muscle Loss in Non-Obese Type 2 Diabetic Patients with NAFLD.

AIM: This study investigated the effects of insulin glargine and exenatide on the muscle mass of patients with newly diagnosed type 2 diabetes (T2DM) and nonalcoholic fatty liver disease (NAFLD). METHODS: We performed a post-hoc analysis of our previously study, a 24-week randomized controlled multicenter clinical trial (ClinicalTrials.gov, NCT02303730). Seventy-six patients were randomly assigned 1:1 to receive insulin glargine or exenatide treatment. The changes in psoas muscle area (PMA) (mm2) were obtained with the cross-sectional Dixonfat magnetic resonance images at the fourth lumber vertebra. RESULTS: There were no significant differences in age, BMI, gender, and PMA in insulin glargine and exenatide groups at baseline. After treatment, PMA tended to increase by 13.13 (-215.52, 280.80) mm2 in the insulin glargine group and decrease by 149.09 (322.90-56.39) mm2 in the exenatide group (both p>0.05). Subgroup analysis showed a 560.64 (77.88, 1043.40) (mm2) increase of PMA in the insulin group relative to the Exenatide group in patients with BMI<28 kg/m2 (p0.031) after adjusting for gender, age, and research center. Interaction analysis showed an interaction between BMI and treatment (p0.009). However, no interaction was observed among subgroups with a BMI≥28 kg/m2 or with different genders and ages. CONCLUSION: Compared to exenatide, insulin glargine can relativity increase PMA in patients with T2DM having BMI<28 kg/m2 and NAFLD.

Circulating immune index predicting the prognosis of patients with hepatocellular carcinoma treated with lenvatinib and immunotherapy.

Background: Immune checkpoint inhibitor (ICI)-based combination therapy has opened a new avenue for the treatment of multiple malignancies including hepatocellular carcinoma (HCC). However, considering the unsatisfactory efficacy, biomarkers are urgently needed to identify the patients most likely to benefit from ICI-based combination therapy. Methods: A total of 194 patients undergoing ICI-based combination therapy for unresectable HCC were retrospectively enrolled and divided into a training cohort (n = 129) and a validation cohort (n = 65) randomly. A novel circulating immune index (CII) defined as the ratio of white blood cell count (×109/L) to lymphocyte proportion (%) was constructed and its prognostic value was determined and validated. Results: Patients with CII ≤ 43.1 reported prolonged overall survival (OS) compared to those with CII > 43.1 (median OS: 24.7 vs 15.1 months; 6-, 12-, 18-month OS: 94.2%, 76.7%, 66.1% vs 86.4%, 68.2%, 22.8%, P = 0.019), and CII was identified as an independent prognostic factor for OS (hazard ratio, 2.24; 95% confidence interval, 1.17-4.31; P = 0.015). These results were subsequently verified in the validation cohort. Additionally, patients with low CII levels had improved best radiological tumor response (complete response, partial response, stable disease, progressive disease: 3%, 36%, 50%, 11% vs 0%, 27%, 46%, 27%; P = 0.037) and disease control rate (89% vs 73%; P = 0.031) in the pooled cohort and better pathologic response (pathologic complete response, major pathologic response, partial pathologic response, no pathologic response: 20%, 44%, 28%, 8% vs 0%, 0%, 40%, 60%; P = 0.005) in the neoadjuvant cohort. Detection of lymphocyte subsets revealed that an elevated proportion of CD4+ T cells was related to better OS, while the proportion of CD8+ T cells was not. Conclusions: We constructed a novel circulating immune biomarker that was capable of predicting OS and therapeutic efficacy for HCC patients undergoing ICI and lenvatinib combination therapy.

Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy.

Background: Lenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions. Methods: 304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency. Results: Multivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low. Conclusion: The PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients.

Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models.

Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.

Characterization of Microvascular Invasion in Hepatocellular Carcinoma Using Computational Modeling of Interstitial Fluid Pressure and Velocity.

BACKGROUND: Most solid tumors show increased interstitial fluid pressure (IFP), and this increased IFP is an obstacle to treatment. A noninvasive model for measuring IFP in hepatocellular carcinoma (HCC) is an unresolved issue. PURPOSE: To develop a noninvasive model to measure IFP and interstitial fluid velocity (IFV) in HCC and to characterize the microvascular invasion (MVI) status by using this model. STUDY TYPE: Retrospective. POPULATION: A total of 97 HCC patients (mean age 57.6 ± 10.9 years, 77.3% males), 53 of them with MVI and 44 of them without MVI. FIELD STRENGTH/SEQUENCE: A 3-T, three-dimensional spoiled gradient-recalled echo. ASSESSMENT: MVI was defined as microscopic vascular invasion of small vessels within the peritumoral liver tissue. The volumes of interest (VOIs) were manually delineated and enclosed the tumor lesion and healthy liver parenchyma, respectively. The extended Tofts model (ETM) was used to estimate permeability parameters from all the VOIs. Subsequently, the continuity partial differential equation (PDE) was implemented and IFP and IFV were acquired. STATISTICAL TESTS: Wilcoxon signed-ranks tests, histogram analysis, Mann-Whitney U test, Fisher's exact test, least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC), Youden index, DeLong test, and Benjamini-Hochberg correction. A P value <0.05 was considered statistically significant. RESULTS: The HCC lesions exhibited elevated IFP and reduced IFV. There were no significant differences in any measured demographic and clinical features between the MVI-positive and MVI-negative groups, except for tumor size. Nine IFP histogram analysis-derived parameters and seven IFV histogram analysis-derived parameters could be used to characterize the MVI status. LASSO regression selected five features: IFP maximum, IFP 10th percentile, IFP 90th percentile, IFV SD, and IFV 10th percentile. The combination of these features showed the highest AUC (0.781) and specificity (77.3%). DATA CONCLUSION: A noninvasive IFP and IFV measurement model for HCC was developed. Specific IFP- and IFV-derived parameters exhibited significant association with the MVI status. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

A new radiomics approach combining the tumor and peri-tumor regions to predict lymph node metastasis and prognosis in gastric cancer.

Objective: The development of non-invasive methods for evaluating lymph node metastasis (LNM) preoperatively in gastric cancer (GC) is necessary. In this study, we developed a new radiomics model combining features from the tumor and peri-tumor regions for predicting LNM and prognoses. Methods: This was a retrospective observational study. In this study, two cohorts of patients with GC treated in Zhongshan Hospital Fudan University (Shanghai, China) were included. In total, 193 patients were assigned to the internal training/validation cohort; another 98 patients were assigned to the independent testing cohort. The radiomics features were extracted from venous phase computerized tomography (CT) images. The radiomics model was constructed and the output was defined as the radiomics score (RS). The performance of the RS and CT-defined N status (ctN) for predicting LNM was compared using the area under the curve (AUC). The 5-year overall survival and progression-free survival were compared between different subgroups using Kaplan-Meier curves. Results: In both cohorts, the RS was significantly higher in the LNM-positive group than that in the LNM-negative group (all P < 0.001). The radiomics model combining features from the tumor and peri-tumor regions achieved the highest AUC in predicting LNM (AUC, 0.779 and 0.724, respectively), which performed better than the radiomics model based only on the tumor region and ctN (AUC, 0.717, 0.622 and 0.710, 0.603, respectively). The differences in 5-year overall survival and progression-free survival between high-risk and low-risk groups were significant (both P < 0.001). Conclusions: The radiomics model combining features from the tumor and peri-tumor regions could effectively predict the LNM in GC. Risk stratification based on the RS was capable of distinguishing patients with poor prognoses.

Accuracy and reproducibility of the O-RADS MRI risk stratification system based on enhanced non-DCE MRI in the assessment of adnexal masses.

OBJECTIVE: Evaluation of the diagnostic performance and reproducibility of the Ovarian-Adnexal Reporting and Data System (O-RADS) Magnetic Resonance Imaging (MRI) risk stratification system based on enhanced non-dynamic contrast-enhanced (non-DCE) MRI in the diagnosis of adnexal masses. METHODS: Patients who underwent conventional pelvic enhanced non-DCE MRI examination within one month prior to surgery formed the study population. Two experienced radiologists independently evaluated the images and assigned a score according to the O-RADS MRI risk stratification system. One of the radiologists reviewed the images and reassigned the scores after three months. Intra- and inter-observer agreement was evaluated with the k coefficient value. The adnexal masses that attained scores between 1 and 3 were considered benign, while those with scores of 4 or 5 were considered malignant. Analyses were conducted to determine the sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curve, which were then used for evaluating the diagnostic efficacy of the developed system based on enhanced non-DCE MRI scan. The reference standard was histology. RESULTS: A total of 308 patients (mean age: 42.09 ± 12.42 years, age range: 20-84 years) were enrolled in the study. Among the 362 adnexal masses from the included patients, there were 320 benign masses and 42 malignant masses. In the case of three readers, there were no malignant tumors scored 1-2. The O-RADS MRI score ≥ 4 was associated with malignancy resulted in a good diagnostic efficacy with the areas under the curve (AUC) values of 0.918 (95 % CI, 0.864-0.972), 0.905 (95 % CI, 0.842-0.968), and 0.882 (95 % CI, 0.815-0.950), the sensitivity values of 90.5 % (95 % CI, 87.5-93.5 %), 85.7 % (95 % CI, 82.1-89.3 %), and 83.3 % (95 % CI, 79.5-87.2 %), and the specificity values of 93.1 % (95 % CI, 90.5-95.7 %), 95.3 % (95 % CI, 93.1-97.5 %), and 93.1 % (95 % CI, 90.5-95.7 %) obtained for the three readers, respectively. Excellent intra-observer agreement and inter-observer agreement were observed with the k values of 0.883 (95 % CI, 0.814-0.952) and 0.848 (95 % CI, 0.770-0.926), respectively. CONCLUSIONS: The O-RADS MRI risk stratification system based on enhanced non-DCE MRI scans exhibited high accuracy and reproducibility in the prediction of adnexal masses malignancy. Enhanced non-DCE MRI scan may offer an alternative diagnostic tool when DCE is not possible.

Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis.

OBJECTIVES: To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1-2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. METHODS: A total of 151 pathologically confirmed 1-2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. RESULTS: The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1-2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1-2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. CONCLUSIONS: Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs. KEY POINTS: • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1-2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.