Computer-Aided Lesion Diagnosis in Automated 3-D Breast Ultrasound Using Coronal Spiculation

Abstract

A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3-D breast ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine classifier and evaluation was done with leave-one-patient-out cross-validation. Receiver operator characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve (A(z)) was 0.93, which was significantly higher than the performance without spiculation features (A(z)=0.90, p=0.02). On a subset of 88 cases, classification performance of CAD (A(z)=0.90) was comparable to the average performance of 10 readers (A(z)=0.87).

Multi-Reader Multi-Case Studies Using the Area under the Receiver Operator Characteristic Curve as a Measure of Diagnostic Accuracy: Systematic Review with a Focus on Quality of Data Reporting

“…Observers read the same patient case on more than one occasion in 50 studies; this information was unclear in the single further study [70] . A fully crossed design (i.e.…”

“…all readers read all patients with all modalities) was used in 47 (92%) studies, but not stated explicitly in 23 of these. A single study [72] did not use a fully crossed design and the design was unclear or unrecorded in 3 [34] , [70] , [76] . Case ordering was randomised (either a different random order across all readers or a different random order for each individual reader) between consecutive readings in 31 (61%) studies, unchanged in 6, and unclear/unrecorded in 14 (27%).…”

“…While the majority of studies (42, 82%) did not present an interpretation of their data framed in terms of changes to individual patient diagnoses, 9 (18%) did so, using outcomes in addition to ROC AUC: For example, as a false-positive to true-positive ratio [35] or the proportion of additional biopsies precipitated and disease detected [64] , or effect on callback rate [43] . The change in AUC was non-significant in 22 studies and in 12 of these the authors speculated why, for example stating that the number of cases was likely to be inadequate [65] , [70] , that the observer task was insufficiently taxing [36] , or that the difference was too subtle to be resolved [45] . For studies where a non-significant change in AUC was observed, authors sometimes framed this as demonstrating equivalence (16 studies, e.g.…”

Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists

“…Artificial intelligence algorithms can be used to discern subtle changes and aid radiologist analysis in various applications. [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] Although the performance of a host of systems approaches the level of radiologists for chest X-rays classification, very limited studies have verified the detection and segmentation of the disease regions compared to human annotation on X-rays. Segmentation is critical for (a) severity assessment of the disease; and for (b) follow-up for treatment monitoring or progression of patient condition.…”

Segmentation of malignant lesions in 3D breast ultrasound using a depth‐dependent model

“…In order to make ABUS reading more effective and reduce large inter and intrareader variability on manual evaluation, computer-aided detection (CADe) 6 and computeraided diagnosis (CADx) 7,8 systems are being developed.…”