ABSTRACT
Purpose: To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer. Materials and methods: 149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA. Results: Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net. Conclusion: The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.
ABSTRACT
Breast cancer has the highest incidence rate among all cancer types worldwide, seriously threatening women's health. The present retrospective study explored differences in serum lipid contents in different breast cancer (BC) subcategories and their correlation with Ki-67 expression levels in patients with invasive BC with the aim of identifying novel diagnostic and prognostic indicators for personalized BC treatment. The study included 170 patients diagnosed with BC who were diagnosed with invasive BC by postoperative pathological examination. Data on patient age, body mass index and menopausal status were collected, in addition to estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 (HER2) and antigen Ki-67 expression levels and pathological tumor type. Preoperative circulating lipid levels, specifically the levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and apolipoproteins A1 (ApoA1) and B (ApoB) were also obtained. Molecular subcategories of BC were grouped based on their immunohistochemistry. Differences in serum lipid levels between the groups were assessed, and correlations between serum lipid and Ki-67 expression levels were explored. While TC, LDL-C, HDL-C and ApoA1 levels differed significantly among molecular subcategories. TG and ApoB levels did not. Circulating TC and LDL-C levels were considerably higher in patients with triple-negative BC (TNBC) and HER2-positive [hormone receptor (HR)-negative] BC than in those with luminal A and B (HER2-negative) BC. Serum HDL-C levels were significantly diminished in the TNBC and HER2-positive (HR-negative) groups compared with the luminal A and B (HER2-negative) groups. ApoA1 levels were significantly reduced in cases of TNBC and HER2-positive (HR-negative) BC compared with luminal A and B BC. Ki-67 expression levels were positively correlated with circulating TC and LDL-C levels and inversely correlated with circulating HDL-C and ApoA1 levels but exhibited no correlation with serum ApoB and TG levels. The results indicate that elevated TC and LDL-C levels and diminished HDL-C and ApoA1 levels were high-risk factors in patients with TNBC and HER2-positive (HR-negative) BC, but not patients with luminal subcategories of BC. Abnormal serum lipid levels were correlated with Ki-67 expression levels, with elevated circulating TC and LDL-C levels and reduced circulating HDL-C and ApoA1 levels indicating a poor prognosis in patients with BC.
ABSTRACT
PURPOSE: To noninvasively evaluate the use of intratumoral and peritumoral regions from full-field digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) magnetic resonance imaging (MRI) images separately and combined to predict the Ki-67 level based on radiomics. PROCEDURES: A total of 209 patients with pathologically confirmed breast cancer were consecutively enrolled from September 2017 to March 2021, who underwent DM, DBT, DCE-MRI, and DW MRI scans. Radiomics features were calculated from intratumoral and peritumoral regions in each modality and selected with the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures (RSs) were built based on intratumoral, peritumoral, and combined intra- and peritumoral regions. The prediction performance of the RSs was evaluated using the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity as comparison metrics. A nomogram was constructed by integrating the multi-model RS and important clinical predictors and assessed by calibration and decision curve analysis. RESULTS: The combined intra- and peritumoral RSs improved the AUC compared with intra- or peritumoral RSs in each modality. The DCE plus DW MRI yielded higher AUC and specificity but lower sensitivity compared with the DM plus DBT. The nomogram incorporating the multi-model RS, age, and lymph node metastasis status achieved the best prediction performance in the training (AUC, nomogram vs. fusion RS vs. clinical model, 0.922 vs. 0.917 vs. 0.672) and validation (AUCs, nomogram vs. fusion RS vs. clinical model, 0.866 vs. 0.838 vs. 0.661) cohorts. DCA analysis confirmed the potential clinical utility of the nomogram. CONCLUSIONS: Peritumoral regions can provide complementary information to intratumoral regions in mammography and MRI for the prediction of Ki-67 levels. The MRI performed better than mammography in terms of AUC and specificity but weaker in sensitivity. The nomogram has a predictive advantage over each modality and could be a potential tool for predicting Ki-67 levels in breast cancer.