Deep Learning Approach for Assessment of Bladder Cancer Treatment Response.

We compared the performance of different Deep learning-convolutional neural network (DL-CNN) models for bladder cancer treatment response assessment based on transfer learning by freezing different DL-CNN layers and varying the DL-CNN structure. Pre- and posttreatment computed tomography scans of 123 patients (cancers, 129; pre- and posttreatment cancer pairs, 158) undergoing chemotherapy were collected. After chemotherapy 33% of patients had T0 stage cancer (complete response).

Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning.

Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses.

Urinary bladder cancer staging in CT urography using machine learning.

Purpose: To evaluate the feasibility of using an objective computer-aided system to assess bladder cancer stage in CT Urography (CTU).

Materials And Methods: A dataset consisting of 84 bladder cancer lesions from 76 CTU cases was used to develop the computerized system for bladder cancer staging based on machine learning approaches. The cases were grouped into two classes based on pathological stage ≥ T2 or below T2, which is the decision threshold for neoadjuvant chemotherapy treatment clinically.

Characterization of Breast Masses in Digital Breast Tomosynthesis and Digital Mammograms: An Observer Performance Study.

Rationale And Objectives: This study aimed to compare Breast Imaging Reporting and Data System (BI-RADS) assessment of lesions in two-view digital mammogram (DM) to two-view wide-angle digital breast tomosynthesis (DBT) without DM.

Materials And Methods: With Institutional Review Board approval and written informed consent, two-view DBTs were acquired from 134 subjects and the corresponding DMs were collected retrospectively. The study included 125 subjects with 61 malignant (size: 3.

Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study.

Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases.

Digital breast tomosynthesis: studies of the effects of acquisition geometry on contrast-to-noise ratio and observer preference of low-contrast objects in breast phantom images.

The effect of acquisition geometry in digital breast tomosynthesis was evaluated with studies of contrast-to-noise ratios (CNRs) and observer preference. Contrast-detail (CD) test objects in 5 cm thick phantoms with breast-like backgrounds were imaged. Twelve different angular acquisitions (average glandular dose for each ~1.

Outcomes of solid palpable masses assessed as BI-RADS 3 or 4A: a retrospective review.

The purpose of this study was to evaluate the outcomes and cancer rate in solid palpable masses with benign features assessed as BI-RADS 3 or 4A. This study was Institutional Review Board approved. Mammography and breast ultrasound reports in our Radiology Information System were searched for solid, palpable masses with benign features described from 1/1/2000 to 12/31/2009, and retrospectively reviewed.

Digital breast tomosynthesis: observer performance of clustered microcalcification detection on breast phantom images acquired with an experimental system using variable scan angles, angular increments, and number of projection views.

Purpose: To investigate the dependence of microcalcification cluster detectability on tomographic scan angle, angular increment, and number of projection views acquired at digital breast tomosynthesis ( DBT digital breast tomosynthesis ).

Materials And Methods: A prototype DBT digital breast tomosynthesis system operated in step-and-shoot mode was used to image breast phantoms. Four 5-cm-thick phantoms embedded with 81 simulated microcalcification clusters of three speck sizes (subtle, medium, and obvious) were imaged by using a rhodium target and rhodium filter with 29 kV, 50 mAs, and seven acquisition protocols.

A similarity study of content-based image retrieval system for breast cancer using decision tree.

Purpose: We are developing a decision tree content-based image retrieval (DTCBIR) CADx system to assist radiologists in characterization of breast masses on ultrasound images.

Methods: Three DTCBIR configurations, including decision tree with boosting (DTb), decision tree with full leaf features (DTL), and decision tree with selected leaf features (DTLs) were compared. For DTb, features of a query mass were combined first into a merged feature score and then masses with similar scores were retrieved.

Breast mass characterization using 3-dimensional automated ultrasound as an adjunct to digital breast tomosynthesis: a pilot study.

Objectives: The purpose of this study was to retrospectively evaluate the effect of 3-dimensional automated ultrasound (3D-AUS) as an adjunct to digital breast tomosynthesis (DBT) on radiologists' performance and confidence in discriminating malignant and benign breast masses.

Methods: Two-view DBT (craniocaudal and mediolateral oblique or lateral) and single-view 3D-AUS images were acquired from 51 patients with subsequently biopsy-proven masses (13 malignant and 38 benign). Six experienced radiologists rated, on a 13-point scale, the likelihood of malignancy of an identified mass, first by reading the DBT images alone, followed immediately by reading the DBT images with automatically coregistered 3D-AUS images.

Triple receptor-negative breast cancer: imaging and clinical characteristics.

Objective: The objective of our study was to retrospectively evaluate the imaging findings of patients with breast cancer negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)-so-called "triple receptor-negative cancer"-and to compare the mammographic findings and clinical characteristics of triple receptor-negative cancer with non-triple receptor-negative cancers (i.e., ER-positive, PR-positive, or HER2-positive or two of the three markers positive).

Similarity evaluation in a content-based image retrieval (CBIR) CADx system for characterization of breast masses on ultrasound images.

Purpose: The authors are developing a content-based image retrieval (CBIR) CADx system to assist radiologists in characterization of breast masses on ultrasound images. In this study, the authors compared seven similarity measures to be considered for the CBIR system. The similarity between the query and the retrieved masses was evaluated based on radiologists' visual similarity assessments.

Computerized image analysis: texture-field orientation method for pectoral muscle identification on MLO-view mammograms.

Purpose: To develop a new texture-field orientation (TFO) method that combines a priori knowledge, local and global information for the automated identification of pectoral muscle on mammograms.

Methods: The authors designed a gradient-based directional kernel (GDK) filter to enhance the linear texture structures, and a gradient-based texture analysis to extract a texture orientation image that represented the dominant texture orientation at each pixel. The texture orientation image was enhanced by a second GDK filter for ridge point extraction.

Dynamic multiple thresholding breast boundary detection algorithm for mammograms.

Purpose: Automated detection of breast boundary is one of the fundamental steps for computer-aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms.

Methods: A large data set of 716 screen-film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used.

Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation.

The goal of this study was to develop an automated method to segment breast masses on dynamic contrast-enhanced (DCE) magnetic resonance (MR) scans and to evaluate its potential for estimating tumor volume on pre- and postchemotherapy images and tumor change in response to treatment. A radiologist experienced in interpreting breast MR scans defined a cuboid volume of interest (VOI) enclosing the mass in the MR volume at one time point within the sequence of DCE-MR scans. The corresponding VOIs over the entire time sequence were then automatically extracted.

A new automated method for the segmentation and characterization of breast masses on ultrasound images.

Segmentation is one of the first steps in most computer-aided diagnosis systems for characterization of masses as malignant or benign. In this study, the authors designed an automated method for segmentation of breast masses on ultrasound (US) images. The method automatically estimated an initial contour based on a manually identified point approximately at the mass center.

Multi-modality CADx: ROC study of the effect on radiologists' accuracy in characterizing breast masses on mammograms and 3D ultrasound images.

Rationale And Objectives: To investigate the effect of a computer-aided diagnosis (CADx) system on radiologists' performance in discriminating malignant and benign masses on mammograms and three-dimensional (3D) ultrasound (US) images.

Materials And Methods: Our dataset contained mammograms and 3D US volumes from 67 women (median age, 51; range: 27-86) with 67 biopsy-proven breast masses (32 benign and 35 malignant). A CADx system was designed to automatically delineate the mass boundaries on mammograms and the US volumes, extract features, and merge the extracted features into a multi-modality malignancy score.

Suspicious breast lesions: assessment of 3D Doppler US indexes for classification in a test population and fourfold cross-validation scheme.

Purpose: To assess the diagnostic performance of various Doppler ultrasonographic (US) vascularity measures in conjunction with grayscale (GS) criteria in differentiating benign from malignant breast masses, by using histologic findings as the reference standard.

Materials And Methods: Institutional Review Board and HIPAA standards were followed. Seventy-eight women (average age, 49 years; range, 26-70 years) scheduled for breast biopsy were included.

Computer-aided detection systems for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms.

Rationale And Objectives: To compare the performance of computer aided detection (CAD) systems on pairs of full-field digital mammogram (FFDM) and screen-film mammogram (SFM) obtained from the same patients.

Materials And Methods: Our CAD systems on both modalities have similar architectures that consist of five steps. For FFDMs, the input raw image is first log-transformed and enhanced by a multiresolution preprocessing scheme.

Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy.

Purpose: To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard.

Materials And Methods: Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25-86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant).

Mammographic density measured with quantitative computer-aided method: comparison with radiologists' estimates and BI-RADS categories.

Purpose: To retrospectively compare computer-aided mammographic density estimation (MDEST) with radiologist estimates of percentage density and Breast Imaging Reporting and Data System (BI-RADS) density classification.

Materials And Methods: Institutional Review Board approval was obtained for this HIPAA-compliant study; patient informed consent requirements were waived. A fully automated MDEST computer program was used to measure breast density on digitized mammograms in 65 women (mean age, 53 years; range, 24-89 years).

Breast masses: computer-aided diagnosis with serial mammograms.

Purpose: To retrospectively evaluate effects of computer-aided diagnosis (CAD) involving an interval change classifier (which uses interval change information extracted from prior and current mammograms and estimates a malignancy rating) on radiologists' accuracy in characterizing masses on two-view serial mammograms as malignant or benign.

Materials And Methods: The data collection protocol had institutional review board approval. Patient informed consent was waived for this HIPAA-compliant retrospective study.

Is lobular carcinoma in situ as a component of breast carcinoma a risk factor for local failure after breast-conserving therapy? Results of a matched pair analysis.

Background: The goals of the current study were to compare the clinicopathologic presentations of patients with lobular carcinoma in situ (LCIS) as a component of breast carcinoma who were treated with breast conserving surgery (BCS) and radiation therapy (RT) with those of patients without LCIS as part of their primary tumor and to report rates of local control by overall cohort and specifically in patients with positive margins for LCIS and multifocal LCIS.

Methods: Sixty-four patients with Stages 0-II breast carcinoma with LCIS (LCIS-containing tumor group, LCTG) that had received BCS+RT treatment at the University of Michigan between 1989 and 2003 were identified. These patients were matched to 121 patients without LCIS (control group) in a 1:2 ratio.

Clinical and radiologic assessments to predict breast cancer pathologic complete response to neoadjuvant chemotherapy.

Purpose: To prospectively compare the ability of clinical examination, mammography, vascularity-sensitive ultrasound, and magnetic resonance imaging (MRI) to determine pathologic complete response (CR) in breast cancer patients undergoing neoadjuvant chemotherapy.

Patients And Methods: Participants were women with primary measurable, operable invasive breast cancer (Stages I-III) who presented to the University of Michigan Breast Care Center. Eligibility criteria were based on clinical need for chemotherapy as part of the overall treatment plan.

Computerized nipple identification for multiple image analysis in computer-aided diagnosis.

Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer.