Hologic

Oct 29, 2008

(Booth 227) Hologic will talk up recently published clinical papers that highlight the utility of computer-aided detection (CAD) in improving the accuracy of screening mammography.

An April 2008 study in the American Journal of Roentgenology used Hologic's R2 software as part of a large-scale breast screening trial that compared a single radiologist using CAD to double reading without CAD. The study compared recall rate, sensitivity, positive predictive value, and cancer detection rate, and found that R2 CAD increased sensitivity in cancer detection with only a small increase in recall rate, according to the Bedford, MA-based company.

In a New England Journal of Medicine study, U.K. researchers used R2 software in a multicenter trial comparing the cancer detection rates of screening with single reading and CAD to that of double reading. The group found that single reading with CAD produced a sensitivity of 87.2%, while double reading recorded a sensitivity of 87.7%. Single reading with CAD had a marginally higher recall rate of 3.9%, versus 3.4% for double reading.

(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.

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Images in a 70-year-old asymptomatic woman with a prior excisional biopsy in the left breast yielding lobular carcinoma in situ 3 years earlier. Screening contrast-enhanced mammography (CEM) was performed, which was considered a prevalence screen as the woman had no prior breast MRI in the past 3 years and no prior CEM. (A) Low-energy (26–30 kVp) mediolateral oblique (MLO) and (B) craniocaudal (CC) views show surgical clips but no abnormality. (C) Recombined MLO and (D) CC images show an enhancing mass in the medial left breast (arrows). Biopsy revealed invasive lobular carcinoma. Size was 0.2 cm; tumor grade II/III; negative lymph nodes; and estrogen receptor–positive, progesterone receptor–positive, and human epidermal growth factor receptor 2–negative.

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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.

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Images in a 70-year-old asymptomatic woman with a prior excisional biopsy in the left breast yielding lobular carcinoma in situ 3 years earlier. Screening contrast-enhanced mammography (CEM) was performed, which was considered a prevalence screen as the woman had no prior breast MRI in the past 3 years and no prior CEM. (A) Low-energy (26–30 kVp) mediolateral oblique (MLO) and (B) craniocaudal (CC) views show surgical clips but no abnormality. (C) Recombined MLO and (D) CC images show an enhancing mass in the medial left breast (arrows). Biopsy revealed invasive lobular carcinoma. Size was 0.2 cm; tumor grade II/III; negative lymph nodes; and estrogen receptor–positive, progesterone receptor–positive, and human epidermal growth factor receptor 2–negative.

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A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.

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MRI false positive for additional disease, (top to bottom): A. Index lesion—grade 2 invasive ductal carcinoma; B. MRI lesion two, upper inner quadrant: Stromal sclerosis and columnar cell change; C. MRI lesion three, upper outer quadrant: No ultrasound correlate, no malignancy on follow-up; D. CEM marked background parenchymal enhancement with a unifocal retroareolar lesion demonstrating wash-out on delayed MLO. Images are republished under a Creative Commons license (CC BY 4.0).

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