Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results

INTRODUCTION

Contrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM.

OBJECTIVES

To develop and test a DL model that classifies CEM images and produces corresponding highlights of lesions detected.

METHODS

Fully annotated 2006 images of 326 females available from the previously published Categorized Digital Database for Contrast Enhanced Mammography images (CDD-CESM) were used for training. We developed a DL multiview contrast mammography model (MVCM) for classification of CEM low energy and recombined images. An external test set of 288 images of 37 females not included in the training was used for validation. Correlation with histopathological results and follow-up was considered the standard reference. The study protocol was approved by the Institutional Review Board and patient informed consent was obtained.

RESULTS

Assessment was done on an external test set of 37 females (mean age, 51.31 years ± 11.07 [SD]) with AUC-ROC for AI performance 0.936; (95 % CI: 0.898, 0.973; p < 0.001) and the best cut off value for prediction of malignancy using AI score = 0.28. Findings were then correlated with histopathological results and follow up which revealed a sensitivity of 75 %, specificity 96.3 %, total accuracy of 90.1 %, positive predictive value (PPV) 87.1 %, and negative predictive value (NPV) 92 %, p-value (<0.001). Diagnostic indices of radiologists were sensitivity 88.9 %, specificity 92.6 %, total accuracy 91.7 %, PPV 80 %, and NPV 96.2 %, p-value (<0.001).

CONCLUSION

A deep learning multiview CEM model was developed and evaluated in a cohort of female participants and showed promising results in detecting breast cancer. This warrants further studies, external training, and validation.

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research

Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist. However, most studies trained the DL models on DM images as no datasets exist for CESM images. We aim to resolve this limitation by releasing a Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images (CDD-CESM) to evaluate decision support systems. The dataset includes 2006 images, with an average resolution of 2355 × 1315, consisting of 310 mass images, 48 architectural distortion images, 222 asymmetry images, 238 calcifications images, 334 mass enhancement images, 184 non-mass enhancement images, 159 postoperative images, 8 post neoadjuvant chemotherapy images, and 751 normal images, with 248 images having more than one finding. This is the first dataset to incorporate data selection, segmentation annotation, medical reports, and pathological diagnosis for all cases. Moreover, we propose and evaluate a DL-based technique to automatically segment abnormal findings in images.

Measurement(s)	Dual-Energy Contrast-Enhanced Digital Spectral Mammography
Technology Type(s)	digital curation
Sample Characteristic - Organism	Homo sapiens • Breast
Sample Characteristic - Location	Egypt

The role of automated breast ultrasound in the assessment of the local extent of breast cancer

Automated breast ultrasound (ABUS) is a non-invasive advanced ultrasound modality. The degree of extension of the cancer within the breast is very important to choose the appropriate kind of surgery/therapy. In the current work, the aim was to evaluate the role of the ABUS in the assessment of the local extent of the breast cancer before management. This is a prospective analysis that studied 562 female patients with proved breast cancers. Evaluation was in regard of the size, multiplicity, and the stromal invasion (ie, the presence of tumor emboli or tumor masses within the stroma of the breast tissue) around the tumor. Cases were subjected to automated breast ultrasound performed in the axial and coronal planes. ABUS showed high accuracy of assessment of the tumor multiplicity (82.2%) and the stromal involvement (93.5%). There was a statistical significance (P < .001) between the ABUS and the pathology regarding the measurement of the size of the index cancer. In conclusion, ABUS could be used for determination of the intramammary extend of the breast cancer. ABUS provided accurate assessment of the peritumor stromal involvement and multiplicity of the cancer which is required to choose the proper choice of surgery.