Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.

PURPOSE: This study aims to develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. MATERIALS AND METHODS: We retrospectively enrolled 196 patients with non-specific invasive breast cancer confirmed by pathology, radiomics and deep learning features were extracted from unenhanced and biphasic (arterial and venous phase) contrast-enhanced CT, and the non-linear support vector machine was used to construct the radiomics signature and the deep learning signature, respectively. Next, a DLRN was developed with independent predictors and evaluated the performance of models in terms of discrimination and clinical utility. RESULTS: Multivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and clinical n stage were independent predictors. The DLRN accurately predicted ALNM and yielded an area under the receiver operator characteristic curve of 0.893 (95% confidence interval, 0.814-0.972) in the validation set, with good calibration. Decision curve analysis confirmed that the DLRN had higher clinical utility than other predictors. CONCLUSIONS: The DLRN had good predictive value for ALNM in breast cancer patients and provide valuable information for individual treatment.

Risk factors and prevention of liver cancer: A bibliometric and visual analysis.

Liver cancer has become an important public health problem. In this study, bibliometrics and visual analysis were performed on the literature related to the risk factors and prevention of liver cancer, in order to understand the latest research progress of the risk factors and prevention of liver cancer. The Web of Science database was used as a retrieval platform to retrieve the published research results from 2012 to 2023. CiteSpace and VOSviewer were utilized for bibliometrics and visual analysis. A total of 2388 articles were screened according to exclusion criteria. Between 2012 and 2018, the number of articles published fluctuated. From 2018 to 2023, the number of published documents showed a steady upward trend. The 3 journals with the most publications are World Journal of Gastroenterology, PLOS ONE, and Hepatology. The United States and China are the countries with the most publications, while Harvard University, the National Institutes of Health and the University of Texas System are the 3 institutions with the most publications. Keywords such as hepatitis B virus, hepatitis C virus, alcohol, obesity, recrudescence rate, global burden are hot words in the field of liver cancer risk factors and prevention. The current research mainly focuses on the influence of environmental factors, behavioral lifestyle and biological factors on liver cancer, as well as the primary and secondary prevention of liver cancer, but there are still many undetermined factors to be explored.

Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: Neoadjuvant chemotherapy (NAC) has become the standard therapeutic option for early high-risk and locally advanced breast cancer. However, response rates to NAC vary between patients, causing delays in treatment and affecting the prognosis for patients who do not sensitive to NAC. MATERIALS AND METHODS: In total, 211 breast cancer patients who completed NAC (training set: 155, validation set: 56) were retrospectively enrolled. we developed a deep learning radiopathomics model(DLRPM) by Support Vector Machine (SVM) method based on clinicopathological features, radiomics features, and pathomics features. Furthermore, we comprehensively validated the DLRPM and compared it with three single-scale signatures. RESULTS: DLRPM had favourable performance for the prediction of pathological complete response (pCR) in the training set (AUC 0.933[95% CI 0.895-0.971]), and in the validation set (AUC 0.927 [95% CI 0.858-0.996]). In the validation set, DLRPM also significantly outperformed the radiomics signature (AUC 0.821[0.700-0.942]), pathomics signature (AUC 0.766[0.629-0.903]), and deep learning pathomics signature (AUC 0.804[0.683-0.925]) (all p < 0.05). The calibration curves and decision curve analysis also indicated the clinical effectiveness of the DLRPM. CONCLUSIONS: DLRPM can help clinicians accurately predict the efficacy of NAC before treatment, highlighting the potential of artificial intelligence to improve the personalized treatment of breast cancer patients.

Development and validation of radiomics machine learning model based on contrast-enhanced computed tomography to predict axillary lymph node metastasis in breast cancer.

Preoperative identification of axillary lymph node metastasis can play an important role in treatment selection strategy and prognosis evaluation. This study aimed to establish a clinical nomogram based on lymph node images to predict lymph node metastasis in breast cancer patients. A total of 193 patients with non-specific invasive breast cancer were divided into training (n = 135) and validation set (n = 58). Radiomics features were extracted from lymph node images instead of tumor region, and the least absolute shrinkage and selection operator logistic algorithm was used to select the extracted features and generate radiomics score. Then, the important clinical factors and radiomics score were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram, and the clinical benefit of using the nomogram was evaluated by decision curve analysis. We found that clinical N stage and radiomics score were independent clinical predictors. Besides, the nomogram accurately predicted axillary lymph node metastasis, yielding an area under the receiver operating characteristic curve of 0.95 (95% confidence interval 0.93-0.98) in the validation set, indicating satisfactory calibration. Decision curve analysis confirmed that the nomogram had higher clinical utility than clinical N stage or radiomics score alone. Overall, the nomogram based on radiomics features and clinical factors can help radiologists to predict axillary lymph node metastasis preoperatively and provide valuable information for individual treatment.