PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.

OBJECTIVE: To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. METHODS: We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. RESULTS: Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. CONCLUSIONS: In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

SMARCA4: Current status and future perspectives in non-small-cell lung cancer.

SMARCA4, also known as transcription activator, is an ATP-dependent catalytic subunit of SWI/SNF (SWItch/Sucrose NonFermentable) chromatin-remodeling complexes that participates in the regulation of chromatin structure and gene expression by supplying energy. As a tumor suppressor that has aberrant expression in ∼10% of non-small-cell lung cancers (NSCLCs), SMARCA4 possesses many biological functions, including regulating gene expression, differentiation and transcription. Furthermore, NSCLC patients with SMARCA4 alterations have a weak response to conventional chemotherapy and poor prognosis. Therefore, the mechanisms of SMARCA4 in NSCLC development urgently need to be explored to identify novel biomarkers and precise therapeutic strategies for this subtype. This review systematically describes the biological functions of SMARCA4 and its role in NSCLC development, metastasis, functional epigenetics and potential therapeutic approaches for NSCLCs with SMARCA4 alterations. Additionally, this paper explores the relationship and regulatory mechanisms shared by SMARCA4 and its mutually exclusive catalytic subunit SMARCA2. We aim to provide innovative treatment strategies and improve clinical outcomes for NSCLC patients with SMARCA4 alterations.

Automatic pulmonary ground-glass opacity nodules detection and classification based on 3D neural network.

PURPOSE: Pulmonary ground-glass opacity (GGO) nodules are more likely to be malignant compared with solid solitary nodules. Due to indistinct boundaries of GGO nodules, the detection and diagnosis are challenging for doctors. Therefore, designing an automatic GGO nodule detection and classification scheme is significantly essential. METHODS: In this paper, we proposed a two-stage 3D GGO nodule detection and classification framework. First, we used a pretrained 3D U-Net to extract lung parenchyma. Second, we adapted the architecture of Mask region-based convolutional neural networks (RCNN) to handle 3D medical images. The 3D model was then applied to detect the locations of GGO nodules and classify lesions (benign or malignant). The class-balanced loss function was also used to balance the number of benign and malignant lesions. Finally, we employed a novel false positive elimination scheme called the feature-based weighted clustering (FWC) to promote the detection accuracy further. RESULTS: The experiments were conducted based on fivefold cross-validation with the imbalanced data set. Experimental results showed that the mean average precision could keep a high level (0.5182) in the phase of detection. Meanwhile, the false positive rate was effectively controlled, and the competition performance metric (CPM) reached 0.817 benefited from the FWC algorithm. The comparative statistical analyses with other deep learning methods also proved the effectiveness of our proposed method. CONCLUSIONS: We put forward an automatic pulmonary GGO nodules detection and classification framework based on deep learning. The proposed method locate and classify nodules accurately, which could be an effective tool to help doctors in clinical diagnoses.