Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification

Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images

Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. This retrospective study collected 279 specimens in 89 non-surgical ESCC patients through endoscopic biopsy between January 2010 and January 2019. These patients were divided into a CRT response group (CR + PR group) and a CRT non-response group (SD + PD group). The WSIs have segmented approximately 1,206,000 non-overlapping patches. Two experienced pathologists manually delineated the eight types of tissues on 32 WSIs, including esophagus tumor cell (TUM), cancer-associated stroma (CAS), normal epithelium layer (NEL), smooth muscle (MUS), lymphocytes (LYM), Red cells (RED), debris (DEB), uneven areas (UNE). The chemoradiotherapy response prediction models were built using maximum relevance-minimum redundancy (MRMR) feature selection and least absolute shrinkage and selection operator (LASSO) regression. However, pathological features with p < 0.1 were selected and integrated to be further screened using a LASSO Cox regression model to build a multivariate Cox proportional hazards model for predicting the OS. The testing accuracy of the tissue classification model was 91.3 %. The pathological model created using two CAS in-depth features and eight TUM in-depth features performed best for the prediction of treatment response and achieved an AUC of 0.744. For the prediction of OS, the testing AUC of this model at one year and three years were 0.675 and 0.870, respectively. The TUM model showed the highest AUC at one year (0.712). With its high accuracy rate, the deep learning model has the potential to transform from bench to bedside in clinical practice, improve patient's quality of life, and prolong the OS rate.

Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study

PURPOSE

This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) patients.

METHODS

A retrospective data set of 1801 cervical LN ultrasound images from 1675 patients with PTC and a prospective test set including 185 images from 160 patients were collected. Four different deep leaning models were trained and validated in the retrospective data set. The best model was selected for CAD system development and compared with three sonographers in the retrospective and prospective test sets.

RESULTS

The Deformable Detection Transformer (DETR) model showed the highest diagnostic efficacy, with a mean average precision score of 86.3% in the retrospective test set, and was therefore used in constructing the CAD system. The detection performance of the CAD system was superior to the junior sonographer and intermediate sonographer with accuracies of 86.3% and 92.4% in the retrospective and prospective test sets, respectively. The classification performance of the CAD system was better than all sonographers with the areas under the curve (AUCs) of 94.4% and 95.2% in the retrospective and prospective test sets, respectively.

CONCLUSIONS

This study developed a Deformable DETR model-based CAD system for automatically detecting and classifying lateral cervical LNs on original ultrasound images, which showed excellent diagnostic efficacy and clinical utility. It can be an important tool for assisting sonographers in the diagnosis process.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning

BACKGROUND

Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated.

PURPOSE

To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences.

STUDY TYPE

Retrospective.

POPULATION

A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female).

FIELD STRENGTH/SEQUENCE

T1-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) with gradient echo sequences, T2-weighted imaging (T2WI) with spin-echo sequences, diffusion-weighted imaging with single-shot echo-planar sequence and at 1.5-T.

ASSESSMENT

A convolutional neural network and long short-term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI-RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE-MRI and non-DCE sequences, respectively.

STATISTICAL TESTS

Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P-value <0.05 was considered statistically significant.

RESULTS

With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE-MRI, the DL-based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE-MRI/T2WI alone, respectively.

DATA CONCLUSION

The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent-free combination is comparable to DCE-MRI alone and the radiologists' reading in AUC and sensitivity.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Multi-Level Swin Transformer Enabled Automatic Segmentation and Classification of Breast Metastases

Detection of metastatic breast cancer lesions is a challenging task in breast cancer treatment. The recent advancements in deep learning gained attention owing to its robustness, particularly in addressing automated segmentation and classification issues in medical images. In this paper, we proposed a modified Swin Transformer model (mST) integrated with a novel Multi-Level Adaptive Feature Fusion (MLAFF) Module. We constructed a modified Swin Transformer network comprising of a Local Transferable MSA (LT-MSA) and a Global Transferable MSA (GT-MSA) in addition to a Feed Forward Network (FFN). Our novel Multi-Level Adaptive Feature Fusion (MLAFF) module iteratively combines the features throughout multiple transformers. We utilized a pre-trained deep learning model U-Net and trained it on mammography utilizing Transfer Learning for automated segmentation. The proposed method, mST-MLAFF, is used for breast cancer classification into normal, benign, and malignant classes. Our model outperformed comparison methods based on U-Net and Swin Transformer in breast metastatic lesion segmentation on the seven benchmark datasets, namely INBreast, DDSM, MIAS, CBIS-DDSM, MIMBCD-UI, KAU-BCMD, and Mammographic Masses. Our model achieved 98% Dice-Similarity coefficient (DSC) for segmentation and an average of 94.5% accuracy for classification, whereas U-Net based model achieved 92% DSC and Swin Transformer achieved 93% DSC. Extensive performance evaluation of our model on benchmark datasets shows the potential of our model for breast cancer classification.Clinical relevance- This research work is focused on assisting the radiologist in the early detection and classification of breast cancer. A single mammography image is analyzed in less than a minute for automated segmentation and classification into malignant and benign classes.