Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules.

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches

Highly accurate model for prediction of lung nodule malignancy with CT scans

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Hybrid Algorithm for the Detection of Lung Cancer Using CNN and Image Segmentation

When it comes to cancer-related fatalities, lung cancer is by far the most common cause. Early detection is the key to a successful diagnosis and treatment plan for lung cancer, just as it is for other types of cancer. Automatic CAD systems for lung cancer screening using computed tomography scans primarily involve two steps: the first step is to detect all potentially malignant pulmonary nodules, and the second step is to determine whether or not the nodules are malignant. There have been a lot of books published recently about the first phase, but not many about the second stage. Screening for lung cancer requires a careful investigation on each suspicious nodule and the integration of information from all nodules. This is because the presence of pulmonary nodules does not always indicate cancer, and the morphology of nodules, including their shape, size, and contextual information, has a complex relationship with cancer. In order to overcome this problem, we suggest a deep CNN architecture that is different from the architectures that are commonly utilised in computer vision. After the suspicious nodules have been formed with the modified version of U-Net, they are used as an input data for our model. First, the suspicious nodules are generated with U-Net. To automatically diagnose lung cancer, the suggested model is a multi-path CNN that concurrently makes use of local characteristics as well as more general contextual characteristics from a wider geographical area. In order to accomplish this, the model consisted of three separate pathways, each of which used a different receptive field size, which contributed to the modelling of distant dependencies (short and long-range dependencies of the neighbouring pixels). After that, we concatenate characteristics from the three different pathways in order to further improve our model's performance. In conclusion, one of the contributions that we have made is the development of a retraining phase system. This system enables us to address issues that are caused by an uneven distribution of picture labels. The experimental findings from the KDSB 2017 challenge demonstrate that our model is more adaptable to the described inconsistency among the nodules' sizes and shapes. Furthermore, our model obtained better results in comparison to other researches

Deep learning for lung cancer on computed tomography:early detection and prognostic prediction

Lung cancer is one of the most fatal cancers in the world, the leading cause of death among both men and women. The five-year survival rate for lung cancer patients is only between 10 and 20%. However, the mortality rate can be reduced if lung cancer is diagnosed at an early stage and treated promptly. Screening trials have been established in many countries to improve early detetion of lung cancer, but it results in numerous scans that need to be evaluated, which is labor-intensive. On the other hand, when lung cancer is diagnosed at an early stage in screening, the clinical response after the treatment can vary between patients. Therefore, strong needs exist for accurate early detection and prognostic prediction of lung cancer.Deep learning recently has achieved great success in medical image analysis, especially for lung cancer. The results described in this thesis show that combining clinical procedures, deep learning techniques are feasible to assist radiologists with pulmonary nodule detection and rule out most negative scans in lung cancer screening. Besides, by integrating clinical factors and imaging features, deep learning can identify high mortality risk lung cancer patients who could benefit from adjuvant chemotherapy. With the implementation of lung cancer screening programs, more imaging and clinical data will be available, which enables deep learning to further boost the efficiency of screening procedures and lower the lung cancer mortality in the future

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

© 2017 The Author(s). This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic

Semi-automated volumetric analysis in the NELSON trial for lung cancer screening: Is there room for diagnostic experience yet

none2openBronte G.; Rolfo C.Bronte, G.; Rolfo, C

Pulmonary nodules and CT screening: the past, present and future

Lung cancer screening has come a long way since the early studies with chest X-ray. Advancing technology and progress in the processing of images have enabled low dose CT to be tried and tested, and evidence suggests its use can result in a significant mortality benefit. There are several issues that need refining in order to successfully implement screening in the UK and elsewhere. Some countries have started patchy implementation of screening and there is increased recognition that the appropriate management of pulmonary nodules is crucial to optimise benefits of early detection, while reducing harm caused by inappropriate medical intervention. This review summarises and differentiates the many recent guidelines on pulmonary nodule management, discusses screening activity in other countries and exposes the present barriers to implementation in the UK