Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images.

OBJECTIVE: To establish a multimodal model for distinguishing benign and malignant breast lesions. MATERIALS AND METHODS: Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05. RESULTS: We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models. CONCLUSION: Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.

Identification of Luminal A breast cancer by using deep learning analysis based on multi-modal images.

Purpose: To evaluate the diagnostic performance of a deep learning model based on multi-modal images in identifying molecular subtype of breast cancer. Materials and methods: A total of 158 breast cancer patients (170 lesions, median age, 50.8 ± 11.0 years), including 78 Luminal A subtype and 92 non-Luminal A subtype lesions, were retrospectively analyzed and divided into a training set (n = 100), test set (n = 45), and validation set (n = 25). Mammography (MG) and magnetic resonance imaging (MRI) images were used. Five single-mode models, i.e., MG, T2-weighted imaging (T2WI), diffusion weighting imaging (DWI), axial apparent dispersion coefficient (ADC), and dynamic contrast-enhanced MRI (DCE-MRI), were selected. The deep learning network ResNet50 was used as the basic feature extraction and classification network to construct the molecular subtype identification model. The receiver operating characteristic curve were used to evaluate the prediction efficiency of each model. Results: The accuracy, sensitivity and specificity of a multi-modal tool for identifying Luminal A subtype were 0.711, 0.889, and 0.593, respectively, and the area under the curve (AUC) was 0.802 (95% CI, 0.657- 0.906); the accuracy, sensitivity, and AUC were higher than those of any single-modal model, but the specificity was slightly lower than that of DCE-MRI model. The AUC value of MG, T2WI, DWI, ADC, and DCE-MRI model was 0.593 (95%CI, 0.436-0.737), 0.700 (95%CI, 0.545-0.827), 0.564 (95%CI, 0.408-0.711), 0.679 (95%CI, 0.523-0.810), and 0.553 (95%CI, 0.398-0.702), respectively. Conclusion: The combination of deep learning and multi-modal imaging is of great significance for diagnosing breast cancer subtypes and selecting personalized treatment plans for doctors.

Biomineralization behavior and mechanism of microbial-mediated removal of arsenate from water.

The microbial-mediated removal of arsenate by biomineralization received much attention, but the molecular mechanism of Arsenic (As) removal by mixed microbial populations remains to be elucidated. In this study, a process for the arsenate treatment using sulfate-reducing bacteria (SRB) containing sludge was constructed, and the performance of As removal was investigated at different molar ratios of AsO43- to SO42-. It was found that biomineralization mediated by SRB could achieve the simultaneous removal of arsenate and sulfate from wastewater but only occurred when microbial metabolic processes were involved. The reducing ability of the microorganisms for the sulfate and arsenate was equivalent, so the precipitates produced at the molar ratio of AsO43- to SO42-of 2:3 were most significant. X-ray absorption fine structure (XAFS) spectroscopy was the first time used to determine the molecular structure of the precipitates which were confirmed to be orpiment (As2S3). Combined with the metagenomics analysis, the microbial metabolism mechanism of simultaneous removal of sulfate and arsenate by the mixed microbial population containing SRB was revealed, that is, the sulfate and As(V) were reduced by microbial enzymes to produce S2- and As(III) to further form As2S3 precipitates. This research provided a reference and theoretical foundation for the simultaneous removal of sulfate and arsenic mediated by SRB-containing sludge in wastewater treatment.

Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model.

Purpose: To develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer. Methods: Clinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks. The idea of a weighted voting method in ensemble learning was employed, and then T2WI, DWI, and DCE-MRI models were used as the base models to construct a multiparametric MRI model. The entire dataset was randomly divided into training sets and testing sets (the training set 202 cases, including 78 ALNM, 124 non-ALNM; the testing set 50 cases, including 20 ALNM, 30 non-ALNM). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of models were calculated. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic performance of each model for axillary lymph node metastasis, and the DeLong test was performed, P< 0.05 statistically significant. Results: For the assessment of axillary lymph node status in invasive breast cancer on the test set, multiparametric MRI models yielded an AUC of 0.913 (95% CI, 0.799-0.974); T2WI-based model yielded an AUC of 0.908 (95% CI, 0.792-0.971); DWI-based model achieved an AUC of 0.702 (95% CI, 0.556-0.823); and the AUC of the DCE-MRI-based model was 0.572 (95% CI, 0.424-0.711). The improvement in the diagnostic performance of the multiparametric MRI model compared with the DWI and DCE-MRI-based models were significant (P< 0.01 for both). However, the increase was not meaningful compared with the T2WI-based model (P = 0.917). Conclusion: Multiparametric MRI image analysis based on an ensemble CNN model with deep learning is of practical application and extension for preoperative prediction of axillary lymph node metastasis in invasive breast cancer.

Functional CdS nanocomposites recovered from biomineralization treatment of sulfate wastewater and its applications in the perspective of photocatalysis and electrochemistry.

The sulfur ions generated during the microbial treatment of sulfate wastewater could cause secondary pollution problem, however, the application of the biomineralization technique could convert sulfur ions into sulfide nanocomposites with diverse properties. This study constructed a multi-stage process for sulfate wastewater treatment and CdS nanocomposites (CdS-NCs) recovery by using biomineralization, which simultaneously achieved the removal of pollutants and recovery of functional nanocomposites. In this process, about 97% of the sulfate could be removed, and the CdS-NCs with a diameter of 16.0-20.2 nm were collected at different pHs. The results of FTIR and Raman proved that the biomacromolecules derived from microorganisms participated in the formation of CdS-NCs. The Mott-Schottky curve suggested that the CdS-NCs belonged to n-type semiconductors with the energy gap of 2.29-2.38 eV and could be applied as the photocatalyst, and up to 78.2% of 200 mg/L tetracycline was photodegraded catalytically by CdS-NCs obtained at pH 6.5. In the application of CdS-NCs as anodes of lithium-ion batteries, all the batteries assembled by CdS-NCs exhibited a very strong cycle performance of more than 500 cycles. This research not only effectively recovered nanocomposites with great application potential from sulfate wastewater but also provided a perspective for the utilization of recovered resources.