IntroductionThe successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature.MethodsThis study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary.Results and discussionThe experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.

IntroductionThe overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness.Methods6 paired PTC samples were employed for whole-exome sequencing, RNA sequencing, and data-dependent acquisition mass spectrum analysis. Weighted gene co-expression network analysis and protein-protein interactions networks were used to screen for hub genes. Moreover, we verified the hub genes' diagnostic and prognostic potential using online databases. Logistic regression was employed to construct a diagnostic model, and we evaluated its efficacy and specificity based on TCGA-THCA and GEO datasets.ResultsThe basic multiomics landscape of PTC among local patients were drawn. The similarities and differences were compared between the Chinese cohort and TCGA-THCA cohorts, including the identification of PNPLA5 as a driver gene in addition to BRAF mutation. Besides, we found 572 differentially expressed genes and 79 differentially expressed proteins. Through integrative analysis, we identified 17 hub genes for prognosis and diagnosis of PTC. Four of these genes, ABR, AHNAK2, GPX1, and TPO, were used to construct a diagnostic model with high accuracy, explicitly targeting PTC (AUC=0.969/0.959 in training/test sets).DiscussionMultiomics analysis of the Chinese cohort demonstrated significant distinctions compared to TCGA-THCA cohorts, highlighting the unique genetic characteristics of Chinese individuals with PTC. The novel biomarkers, holding potential for diagnosis and prognosis of PTC, were identified. Furthermore, these biomarkers provide a valuable tool for precise medicine, especially for immunotherapeutic or nanomedicine based cancer therapy.

ObjectiveCancer-associated mesenchymal stem cells (MSCs) regulate the progression of cancers through exosome-delivered components, while few studies are conducted on hepatocellular carcinoma (HCC). This study aimed to evaluate the effect of exosomes from HCC-associated MSCs (HCC-MSCs) on HCC cellular functions and the potential regulatory mechanism.MethodsHCC cells (Huh7 and PLC) were cultured normally or co-cultured with HCC-MSCs, HCC-MSCs plus GW4869, or HCC-MSC-derived exosomes; then mRNA sequencing and RT-qPCR validation were conducted. Subsequently, candidate genes were sorted out and modified in HCC cells. Next, TMBIM6-modified HCC-MSCs were used to treat HCC cells.ResultsBoth HCC-MSCs and their derived exosomes promoted proliferation, invasion, sphere formation ability but suppressed apoptosis in HCC cells (all p < 0.05); however, the effect of HCC-MSCs on these cellular functions was repressed by exosome inhibitor (GW4869). Subsequently, TMBIM6, EEF2, and PRDX1 were sorted out by mRNA sequencing and RT-qPCR validation as candidate genes implicated in the regulation of HCC cellular functions by HCC-MSC-derived exosomes. Among them, TMBIM6 had a potent effect (all p < 0.05), while EEF2 and PRDX1 had less effect on regulating HCC cell viability and invasion. Next, direct silencing TMBIM6 repressed viability, sphere formation, invasion, epithelial–mesenchymal transition (EMT), and PI3K/AKT pathway but promoted apoptosis in HCC cells; however, overexpressing TMBIM6 showed the opposite effect. Furthermore, incubating with exosomes from TMBIM6-modified HCC-MSCs presented a similar effect as direct TMBIM6 modification in HCC cells.ConclusionHCC-MSC-derived exosomes transmit TMBIM6 to promote malignant behavior via PI3K/AKT pathway in HCC.

IntroductionThe hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature.MethodsWe retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC).ResultsIn the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively.ConclusionHBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.

IntroductionThe prognosis of patients with non-central recurrent cervical cancer (NRCC) remains poor, and treatment options are limited. We aimed to explore the accuracy and safety of the 3D-printed non-coplanar template (3D-PNCT)-assisted 192Ir interstitial brachytherapy (ISBT) in the treatment of NRCC.Material and methodsA total of 36 patients with NRCC who received 3D-PNCT-guided 192Ir ISBT in the First Affiliated Hospital of Zhengzhou University from January 2021 to July 2022 were included in this study. There were 36 3D-PNCTs that were designed and printed. The prescribed dose was 30–36 Gy, divided into five to six times, once a week. To evaluate whether the actual parameters were consistent with the preoperative design, the dosimetric parameters of pre- and postoperative treatment plans were compared, including dose of 90% high-risk clinical target volume (HR-CTV D90), volume percentage of 100% and 150% prescribed dose V100% and V150%, homogeneity index (HI), conformal index (CI), external index (EI), and dose received by 2 cm3 (D2cm3) of the rectum, colon, bladder, and ileum. The safety parameters including occurrence of bleeding, infection, pain, radiation enteritis, and radiation cystitis within 3 months after operation were recorded.ResultsAll patients successfully completed the treatment and achieved the goals of the preoperative plan. There was no significant difference in the accuracy (HRCTVD90, V100%, EI, CI, and HI) and safety (D2cm3 of rectum, colon, bladder, and ileum) parameters of the postoperative plan compared with the preoperative plan (all p>0.05). Major side effects included bleeding at the puncture site (13.9%), postoperative pain (8.3%), acute radiation cystitis (13.9%), and radiation enteritis (19.4%). There were no serious perioperative complications and no grade 3–4 acute radiotherapy side effects.Conclusion3D-PNCT-assisted 192Ir ISBT can be accurately and safely applied in the treatment of patients with NRCC.