Mehdi Alilou

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT

Purpose Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT. Methods The nodule segmentation method starts with separation of lung regions from the surrounding lung anatomy. Next, the lung CT scans are projected into and represented in a three dimensional spectral embedding (SE) space, allowing for better determination of the boundaries of the nodule. This then enables the application of a gradient vector flow active contour (SEGvAC) model for nodule boundary extraction. A set of 24 shape features from both 2D slices and 3D surface of the segmented nodules are extracted, including features pertaining to the angularity, spiculation, elongation and nodule compactness. A feature selection scheme, PCA‐VIP, is employed to identify the most discriminating set of features to distinguish granulmoas from adenocarcinomas within a learning set of 82 patients. The features thus identified were then combined with a support vector machine classifier and independently validated on a distinct test set comprising 67 patients. The performance of the classifier for both of the training and validation cohorts was evaluated by the area under receiver characteristic curve (ROC). Results We used 82 and 67 studies from two different institutions respectively for training and independent validation of the model and the shape features. The Dice coefficient between automatically segmented nodules by SEGvAC and the manual delineations by expert radiologists (readers) was 0.84± 0.04 whereas inter‐reader segmentation agreement was 0.79± 0.12. We also identified a set of consistent features (Roughness, Convexity and Spherecity) that were found to be strongly correlated across both manual and automated nodule segmentations (R > 0.80, p < 0.0001) and capture the marginal smoothness and 3D compactness of the nodules. On the independent validation set of 67 studies our classifier yielded a ROC AUC of 0.72 and 0.64 for manually‐ and automatically segmented nodules respectively. On a subset of 20 studies, the AUCs for the two expert radiologists and 1 pulmonologist were found to be 0.82, 0.68 and 0.58 respectively. Conclusions The major finding of this study was that certain shape features appear to differentially express between granulomas and adenocarcinomas and thus computer extracted shape cues could be used to distinguish these radiographically similar pathologies.

Intra-perinodular Textural Transition (Ipris): A 3D Descriptor for Nodule Diagnosis on Lung CT

This paper presents Ipris (Intra-perinodular textural transition), a new radiomic method, to automatically distinguish between benign and malignant nodules on routine lung CT scans. Ipris represents a minimal set of quantitative measurements which attempt to capture the transition in textural appearance going from the inside to the outside of the nodule. Briefly the approach involves partitioning the 3D volume and interface of the nodule into K nested shells. Then, a set of 48 Ipris features from 2D slices of the shells are extracted. The features pertain to the spiculations, intensity and gradient sharpness obtained from intensity differences between inner and outer voxels of an interface voxel. The Ipris features were used to train a support vector machine classifier in order to distinguish between benign (granulomas) from malignant (adenocarcinomas) nodules on non-contrast CT scans. We used CT scans of 290 patients from multiple institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. Independent validation of the Ipris approach yielded an AUC of 0.83 whereas, the established textural and shape radiomic features yielded a corresponding AUC of 0.75, while the AUCs for two human experts (1 pulmonologist, 1 radiologist) yielded corresponding AUCs of 0.69 and 0.73.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Abstract. Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training (N  =  139) and the other (N  =  56) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.

Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response

As a “hallmark of cancer”, tumor-induced angiogenesis is one of the most important mechanisms of a tumor’s adaptation to changes in nutrient requirement. The angiogenic activity of certain tumors has been found to be predictive of a patient’s ultimate response to therapeutic intervention. This then begs the question if there are differences in vessel arrangement and corresponding convolutedness, between tumors that appear phenotypically similar, but respond differently to treatment. Even though textural radiomics and deep learning-based approaches have been shown to distinguish disease aggressiveness and assess therapeutic response, these descriptors do not specifically interpret differences in vessel characteristics. Moreover, most existing approaches have attempted to model disease characteristics just within tumor confines, or right outside, but do not consider explicit parenchymal vessel morphology. In this work, we introduce VaNgOGH (Vascular Network Organization via Hough transform), a new descriptor of architectural disorder of the tumor’s vascular network. We demonstrate the efficacy of VaNgOGH in two clinically challenging problems: (a) Predicting pathologically complete response (pCR) in breast cancer prior to treatment (BCa, N = 76) and (b) distinguishing benign nodules from malignant non-small cell lung cancer (LCa, N = 81). For both tasks, VaNgOGH had test area under the receiver operating characteristic curve (\(AUC_{BCa}\) = 0.75, \(AUC_{LCa}\) = 0.68) higher than, or comparable to, state of the art radiomic approaches (\(AUC_{BCa}\) = 0.75, \(AUC_{LCa}\) = 0.62) and convolutional neural networks (\(AUC_{BCa}\) = 0.67, \(AUC_{LCa}\) = 0.66). Interestingly, when a known radiomic signature was used in conjunction with VaNgOGH, \(AUC_{BCa}\) increased to 0.79.

Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas

Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced. Our study comprised of CT scans of 290 patients from two different institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. In conjunction with a machine learning classifier, the top informative and stable QVT features yielded an area under receiver operating characteristic curve (ROC AUC) of 0.85 in the independent validation set. On the same cohort, the corresponding AUCs for two human experts including a radiologist and a pulmonologist were found to be 0.61 and 0.60, respectively. QVT features also outperformed well known shape and textural radiomic features which had a maximum AUC of 0.73 (p-value = 0.002), as well as features learned using a convolutional neural network AUC = 0.76 (p-value = 0.028). Our results suggest that QVT features could potentially serve as a non-invasive imaging biomarker to distinguish granulomas from adenocarcinomas on non-contrast CT scans.