Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers

Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple nodular patients, which ignores the relations between nodules. In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules. By treating the multiple nodules from a same patient as a whole, critical relational information between solitary-nodule voxels is extracted. To our knowledge, it is the first study to learn the relations between multiple pulmonary nodules. Inspired by recent advances in natural language processing (NLP) domain, we introduce a self-attention transformer equipped with 3D CNN, named {NoduleSAT}, to replace typical pooling-based aggregation in multiple instance learning. Extensive experiments on lung nodule false positive reduction on LUNA16 database, and malignancy classification on LIDC-IDRI database, validate the effectiveness of the proposed method.Comment: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020

Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., convolutional neural networks (CNNs), dominate recent research in Computer-Aided Diagnosis (CADx). Unfortunately, as black-box predictors, we argue that CNNs are "diagnosing" voxels (or pixels), rather than lesions; in other words, visual saliency from a trained CNN is not necessarily concentrated on the lesions. On the other hand, classification in clinical applications suffers from inherent ambiguities: radiologists may produce diverse diagnosis on challenging cases. To this end, we propose a controllable and explainable {\em Probabilistic Radiomics} framework, by combining the Radiomics analysis and probabilistic deep learning. In our framework, 3D CNN feature is extracted upon lesion region only, then encoded into lesion representation, by a controllable Non-local Shape Analysis Module (NSAM) based on self-attention. Inspired from variational auto-encoders (VAEs), an Ambiguity PriorNet is used to approximate the ambiguity distribution over human experts. The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable. We apply the proposed method on lung nodule diagnosis on LIDC-IDRI database to validate its effectiveness.Comment: MICCAI 2019 (early accept), with supplementary material

RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccessible to communities, or focus on rib segmentation that neglects the clinical significance of rib labeling. To address these issues, we extend our prior dataset (RibSeg) on the binary rib segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT scans (15,466 individual ribs in total) and annotations manually inspected by experts for rib labeling and anatomical centerline extraction. Based on the RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction. To improve computational efficiency, we propose a sparse point cloud representation of CT scans and compare it with standard dense voxel grids. Moreover, we design and analyze evaluation metrics to address the key challenges of each task. Our dataset, code, and model are available online to facilitate open research at https://github.com/M3DV/RibSegComment: 10 pages, 6 figures, journa

Development of a combined model incorporating clinical characteristics and magnetic resonance imaging features to enhance the predictive value of a prognostic model for locally advanced cervical cancer

ObjectiveThis study aimed to develop non-invasive predictive tools based on clinical characteristics and magnetic resonance imaging (MRI) features to predict survival in patients with locally advanced cervical cancer (LACC), thereby facilitating clinical decision-making.MethodsWe conducted a retrospective analysis of clinical and MRI data from LACC patients who underwent radical radiotherapy at our center between September 2012 and May 2020. Prognostic predictors were identified using single-factor and multifactor Cox analyses. Clinical and MRI models were established based on relevant features, and combined models were created by incorporating MRI factors into the clinical model. The predictive performance of the models was evaluated using the area under the curve (AUC), consistency index (C-index), and decision curve analysis (DCA).ResultsThe study included 175 LACC patients. Multivariate Cox analysis revealed that patients with FIGO IIA-IIB stage, ECOG score 0-1, CYFRA 21-1<7.7 ng/ml, ADC ≥ 0.79 mm^2/s, and Kep ≥ 4.23 minutes had a more favorable survival prognosis. The clinical models, incorporating ECOG, FIGO staging, and CYFRA21-1, outperformed individual prognostic factors in predicting 5-year overall survival (AUC: 0.803) and 5-year progression-free survival (AUC: 0.807). The addition of MRI factors to the clinical model (AUC: 0.803 for 5-year overall survival) increased the AUC of the combined model to 0.858 (P=0.011). Similarly, the combined model demonstrated a superior predictive ability for 5-year progression-free survival, with an AUC of 0.849, compared to the clinical model (AUC: 0.807) and the MRI model (AUC: 0.673). Furthermore, the C-index of the clinical models for overall survival and progression-free survival were 0.763 and 0.800, respectively. Upon incorporating MRI factors, the C-index of the combined model increased to 0.826 for overall survival and 0.843 for progression-free survival. The DCA further supported the superior prognostic performance of the combined model.ConclusionOur findings indicate that ECOG, FIGO staging, and CYFRA21-1 in clinical characteristics, as well as ADC and Kep values in MRI features, are independent prognostic factors for LACC patients undergoing radical radiotherapy. The combined models provide enhanced predictive ability in assessing the risk of patient mortality and disease progression

Long Noncoding RNA FAM201A Mediates the Radiosensitivity of Esophageal Squamous Cell Cancer by Regulating ATM and mTOR Expression via miR-101

Background: The aim of the present study was to identify the potential long non-coding (lnc.)-RNA and its associated molecular mechanisms involved in the regulation of the radiosensitivity of esophageal squamous cell cancer (ESCC) in order to assess whether it could be a biomarker for the prediction of the response to radiotherapy and prognosis in patients with ESCC.Methods: Microarrays and bioinformatics analysis were utilized to screen the potential lncRNAs associated with radiosensitivity in radiosensitive (n = 3) and radioresistant (n = 3) ESCC tumor tissues. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed in 35 ESCC tumor tissues (20 radiosensitive and 15 radioresistant tissues, respectively) to validate the lncRNA that contributed the most to the radiosensitivity of ESCC (named the candidate lncRNA). MTT, flow cytometry, and western blot assays were conducted to assess the effect of the candidate lncRNA on radiosensitivity in vitro in ECA109/ECA109R ESCC cells. A mouse xenograft model was established to confirm the function of the candidate lncRNA in the radiosensitivity of ESCC in vivo. The putative downstream target genes regulated by the candidate lncRNA were predicted using Starbase 2.0 software and the TargetScan database. The interactions between the candidate lncRNA and the putative downstream target genes were examined by Luciferase reporter assay, and were confirmed by PCR.Results: A total of 113 aberrantly expressed lncRNAs were identified by microarray analysis, of which family with sequence similarity 201-member A (FAM201A) was identified as the lncRNA that contributed the most to the radiosensitivity of ESCC. FAM201A was upregulated in radioresistant ESCC tumor tissues and had a poorer short-term response to radiotherapy resulting in inferior overall survival. FAM201A knockdown enhanced the radiosensitivity of ECA109/ECA109R cells by upregulating ataxia telangiectasia mutated (ATM) and mammalian target of rapamycin (mTOR) expression via the negative regulation of miR-101 expression. The mouse xenograft model demonstrated that FAM201A knockdown improved the radiosensitivity of ESCC.Conclusion: The lncRNA FAM201A, which mediated the radiosensitivity of ESCC by regulating ATM and mTOR expression via miR-101 in the present study, may be a potential biomarker for predicting radiosensitivity and patient prognosis, and may be a therapeutic target for enhancing cancer radiosensitivity in ESCC

MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA–Disease Association Prediction

MicroRNAs (miRNAs) are small non-coding RNAs that are related to a number of complicated biological processes, and numerous studies have demonstrated that miRNAs are closely associated with many human diseases. In this study, we present a matrix decomposition and similarity-constrained matrix factorization (MDSCMF) to predict potential miRNA–disease associations. First of all, we utilized a matrix decomposition (MD) algorithm to get rid of outliers from the miRNA–disease association matrix. Then, miRNA similarity was determined by utilizing similarity kernel fusion (SKF) to integrate miRNA function similarity and Gaussian interaction profile (GIP) kernel similarity, and disease similarity was determined by utilizing SKF to integrate disease semantic similarity and GIP kernel similarity. Furthermore, we added L2 regularization terms and similarity constraint terms to non-negative matrix factorization to form a similarity-constrained matrix factorization (SCMF) algorithm, which was applied to make prediction. MDSCMF achieved AUC values of 0.9488, 0.9540, and 0.8672 based on fivefold cross-validation (5-CV), global leave-one-out cross-validation (global LOOCV), and local leave-one-out cross-validation (local LOOCV), respectively. Case studies on three common human diseases were also implemented to demonstrate the prediction ability of MDSCMF. All experimental results confirmed that MDSCMF was effective in predicting underlying associations between miRNAs and diseases