Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer.

Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P < 0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P = 0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P = 0.002), and solidity (0.983 vs. 0.934, P = 0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.

Size-adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset.

PURPOSE: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend to develop and evaluate a novel deep learning method for a more challenging task, detecting various benign and malignant mediastinal lesions with wide variations in sizes, shapes, intensities, and locations in chest CT images. METHODS: Our method for mediastinal lesion detection contains two main stages: (a) size-adaptive lesion candidate detection followed by (b) false-positive (FP) reduction and benign-malignant classification. For candidate detection, an anchor-free and one-stage detector, namely 3D-CenterNet is designed to locate suspicious regions (i.e., candidates with various sizes) within the mediastinum. Then, a 3D-SEResNet-based classifier is used to differentiate FPs, benign lesions, and malignant lesions from the candidates. RESULTS: We evaluate the proposed method by conducting five-fold cross-validation on a relatively large-scale dataset, which consists of data collected on 1136 patients from a grade A tertiary hospital. The method can achieve sensitivity scores of 84.3% ± 1.9%, 90.2% ± 1.4%, 93.2% ± 0.8%, and 93.9% ± 1.1%, respectively, in finding all benign and malignant lesions at 1/8, 1/4, ½, and 1 FPs per scan, and the accuracy of benign-malignant classification can reach up to 78.7% ± 2.5%. CONCLUSIONS: The proposed method can effectively detect mediastinal lesions with various sizes, shapes, and locations in chest CT images. It can be integrated into most existing pulmonary lesion detection systems to promote their clinical applications. The method can also be readily extended to other similar 3D lesion detection tasks.

Three-dimensionally printed navigational template: a promising guiding approach for lung biopsy.

Background: Percutaneous transthoracic lung biopsy is customarily conducted under computed tomography (CT) guidance, which primarily depends on the conductors' experience and inevitably contributes to long procedural duration and radiation exposure. Novel technique facilitating lung biopsy is currently demanded. Methods: Based on the reconstructed anatomical information of CT scans, a three-dimensionally printed navigational template was customized to guide fine-needle aspiration (FNA). The needle insertion site and angle could be indicated by the template after proper placement according to the reference landmarks. From June 2020 to August 2020, patients with peripheral indeterminate lung lesions ≥30 mm in diameter were enrolled in a pilot trial. Cases were considered successful when the virtual line indicated by the template in the first CT scan was pointing at the target, and the rate of success was recorded. The insertion deviation, procedural duration, radiation exposure, biopsy-related complications, and diagnostic yield were documented as well. Results: A total of 20 patients consented to participate, and 2 withdrew. The remaining 18 participants consisting of 11 men and 7 women with a median age of 63 [inter-quartile range (IQR), 50-68] years and a median body mass index (BMI) of 23.5 (IQR, 20.8-25.8) kg/m2 received template-guided FNA. The median nodule size of the patients was 41.2 (IQR, 36.2-51.9) mm and 17 lesions were successfully targeted (success rate, 94.4%). One lesion was not reached through the designed trajectory due to an unpredictable alteration of the lesion's location resulting from pleural effusion. The median deviation between the actual position of the needle tip and the designed route was 9.4 (IQR, 6.8-11.7) mm. The median procedural duration was 10.7 (IQR, 9.7-11.8) min, and the median radiation exposure was 220.9 (IQR, 198.6-249.5) mGy×cm. No major biopsy-related complication was encountered. Definitive diagnosis of malignancy was reached in 13 of the 17 (76.5%) participants. Conclusions: The feasibility and safety of navigational template-guided FNA were preliminarily validated in lung biopsy cohort. Nonetheless, patients with pleural effusion were not recommended to undergo FNA guided by such technique. Trial Registration: This study was registered with ClinicalTrials.gov (identifier: NCT03325907).

IMAL-Net: Interpretable multi-task attention learning network for invasive lung adenocarcinoma screening in CT images.

PURPOSE: Feature maps created from deep convolutional neural networks (DCNNs) have been widely used for visual explanation of DCNN-based classification tasks. However, many clinical applications such as benign-malignant classification of lung nodules normally require quantitative and objective interpretability, rather than just visualization. In this paper, we propose a novel interpretable multi-task attention learning network named IMAL-Net for early invasive adenocarcinoma screening in chest computed tomography images, which takes advantage of segmentation prior to assist interpretable classification. METHODS: Two sub-ResNets are firstly integrated together via a prior-attention mechanism for simultaneous nodule segmentation and invasiveness classification. Then, numerous radiomic features from the segmentation results are concatenated with high-level semantic features from the classification subnetwork by FC layers to achieve superior performance. Meanwhile, an end-to-end feature selection mechanism (named FSM) is designed to quantify crucial radiomic features greatly affecting the prediction of each sample, and thus it can provide clinically applicable interpretability to the prediction result. RESULTS: Nodule samples from a total of 1626 patients were collected from two grade-A hospitals for large-scale verification. Five-fold cross validation demonstrated that the proposed IMAL-Net can achieve an AUC score of 93.8% ± 1.1% and a recall score of 93.8% ± 2.8% for identification of invasive lung adenocarcinoma. CONCLUSIONS: It can be concluded that fusing semantic features and radiomic features can achieve obvious improvements in the invasiveness classification task. Moreover, by learning more fine-grained semantic features and highlighting the most important radiomics features, the proposed attention and FSM mechanisms not only can further improve the performance but also can be used for both visual explanations and objective analysis of the classification results.