Ying Zeng

Background: Salivary microRNAs (miRNAs) have been investigated for oral cancer detection, but comparison with healthy volunteers does not establish performance among patients with oral mucosal lesions. This review maps evidence relevant to discrimination between oral squamous cell carcinoma or oral cancer (OSCC/OC) and oral potentially malignant disorders (OPMDs). Methods: The evidence was organized as a scoping review with study-specific diagnostic descriptions. Clinical comparators, assay methods, thresholds, participant overlap and data provenance were charted. No estimates were pooled. Searches were executed on 8 September 2026 in PubMed ( n = 487), Web of Science Core Collection ( n = 362), Scopus ( n = 318) and the Cochrane Central Register of Controlled Trials ( n = 34), with 28 additional records identified through citation chasing and reference-list checking, for a total of 1,229 records. After removal of 412 duplicates and 15 records before screening, 802 titles and abstracts were screened; 54 full-text reports were sought, of which 3 could not be retrieved and 51 were assessed. Sixteen sources were included in the evidence map, comprising 13 primary studies and 3 contextual systematic reviews. Results: Three diagnostic reports contained five assay-level results for cancer vs. OPMD or oral lichen planus. A four-miRNA panel was reported to have 90% sensitivity and 82.7% specificity in 50 OC and 52 OPMD participants. Two individual miRNAs evaluated in the same 30 OSCC and 30 OPMD participants each had reported sensitivity of 70% and specificity of 60%. An additional oral lichen planus comparison had uncertain ROC analysis denominators and was retained without reconstructed counts. Repeated assays within a cohort were not considered independent studies. Small selected samples, data-derived thresholds, inconsistent reporting and limited independent validation restricted interpretation. Conclusions: Salivary miRNAs have shown study-specific discrimination between cancer and selected OPMD groups. Evidence does not establish a transportable accuracy estimate or clinical benefit for lesion triage. Prospective evaluation in consecutive lesion cohorts is needed before these assays can inform biopsy, referral or surveillance decisions.

This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC). In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined. The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as evidenced by higher AUC values in the training set (0.820 versus 0.810 and 0.700, p = 0.049 and p = 0.031, respectively) and in the test set (0.805 versus 0.771 and 0.732, p = 0.041 and p = 0.025, respectively). Finally, the hybrid model consistently demonstrated robust predictive capabilities in identifying presence of MP/S components, achieving AUC of 0.830 in the training set and 0.849 in the test set (all p < 0.05). The ITH derived from sub-region within the tumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC.

Background: This study aims to quantify intratumoral heterogeneity(ITH) using preoperative CT scans and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC). Methods: In this retrospective study, 457 patients postoperatively diagnosed with clinical stage I solid LADC were included from two medical centers, comprising a training set (center 1, n=304) and a test set (center 2, n=153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined. Results: The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as indicated by higher AUC values in the training (0.820 versus 0.810 and 0.700) and test sets (0.805 versus 0.771 and 0.732), respectively. Notably, the hybrid model consistently demonstrated robust predictive capabilities in identifying MP/S (+), achieving AUCs of 0.830 in the training set and 0.849 in the test sets. Conclusion: The ITH of sub-regions within the intratumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC. This finding holds the potential to make a significant contribution to clinical decision-making processes.

Background: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non‐invasive assessment method.Purpose: To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI‐MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast‐enhanced MRI (DCE‐MRI).Study Type: Cross‐sectional retrospective cohort study.Population: The study included 206 BC patients, with 136 in the training set [97 LVI(−) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(−) and 18 LVI(+) cases; median age: 48 years].Field Strength/Sequence: 1.5 T/T1‐weighted images, fat‐suppressed T2‐weighted images, diffusion‐weighted imaging (DWI), and DCE‐MRI.Assessment: The MRI‐MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE‐MRI datasets, specifically the first and second post‐contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI‐MF, Radiomics, and DL approaches.Statistical Tests: Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis.Results: Rim sign and peritumoral edema features were used to develop the MRI‐MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI‐MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI‐MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI‐MF (AUC = 0.796), DL + MRI‐MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826).Data Conclusion: The joint model incorporating MRI‐MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery.Level of Evidence: 4Technical Efficacy: Stage 2