Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

Inflammation plays an important role in the pathogenesis of ovarian cancer. This study sought to investigate the association between the preoperative c-reactive protein/albumin ratio (CRP/Alb) and oncological outcomes in ovarian cancer patients. Two hundred patients with histologically verified ovarian cancer between June 2006 and July 2012 were retrospectively reviewed. Overall survival was evaluated by the Kaplan–Meier method and log-rank test. The significance of risk factors for overall survival was evaluated with the Cox proportional hazards model. Additionally, area under the receiver operating characteristic curve (AUC) was used to compare the predictive ability of CRP/Alb, Glasgow Prognostic Score (GPS), modified GPS (mGPS), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), prognostic index (PI) and prognostic nutritional index (PNI). The optimal cutoff value of CRP/Alb was 0.68. Increased CRP/Alb (≥0.68) was associated with advanced stage, residual tumor, ascites, elevated serum carbohydrate antigen(CA)-125 level, GPS, and mGPS (all p < 0.05). Patients with high CRP/Alb had poor overall survival compared to those with low CRP/Alb (p < 0.001). Multivariable analysis showed that CRP/Alb (Hazard Ratio (HR) 1.330, 95% confidence interval (CI) 1.131–1.564, p = 0.001), tumor stage (HR 1.577, 95% CI 1.189–2.091, p = 0.002), residual tumor (HR 2.337, 95% CI 1.518–3.597, p < 0.001) and age (HR 1.017, 95% CI 1.000–1.035, p = 0.046) were independent prognostic factors for overall survival. Additionally, the CRP/Alb showed greater AUC values at 1 year (0.692), 3 years (0.659), and 5 years (0.682) than GPS, mGPS and PNI. The CRP/Alb is a novel independent marker of poor prognosis among ovarian cancer patients and shows superior prognostic ability compared to the established inflammation-based prognostic indices.

The contrast-enhanced US Liver Imaging Reporting and Data System version 2017 diagnostic algorithm is effective in predicting hepatocellular carcinoma, although diagnostic performance may be improv... Background Use of contrast material–enhanced (CE) US Liver Imaging Reporting and Data System (LI-RADS) version 2017 has not been validated in large populations where hepatitis B virus (HBV) is ende...

For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (279 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865–0.968) in the internal validation cohort, and 0.900 (95% CI 0.816–0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719–0.889], p < 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704–0.878], p < 0.013) in the external test cohort. The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. 10.13039/501100003453 Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and 10.13039/501100001809 National Natural Science Foundation of China (No. 91959127; No. 81971631).

Radiofrequency ablation (RFA) is a thermal ablation technique widely used for the management of benign thyroid nodules. To date, five academic societies in various countries have reported clinical practice guidelines, opinion statements, or recommendations regarding the use of thyroid RFA. However, despite some similarities, there are also differences among the guidelines, and a consensus is required regarding safe and effective treatment in Asian countries. Therefore, a task force was organized by the guideline committee of the Asian Conference on Tumor Ablation with the goal of devising recommendations for the clinical use of thyroid RFA. The recommendations in this article are based on a comprehensive analysis of the current literature and the consensus opinion of the task force members.