Automated vascular analysis of breast thermograms with interpretable features.

Purpose: Vascular changes are observed from initial stages of breast cancer, and monitoring of vessel structures helps in early detection of malignancies. In recent years, thermal imaging is being evaluated as a low-cost imaging modality to visualize and analyze early vascularity. However, visual inspection of thermal vascularity is challenging and subjective. Therefore, there is a need for automated techniques to assist physicians in visualization and interpretation of vascularity by marking the vessel structures and by providing quantified qualitative parameters that helps in malignancy classification Approach: In the literature, there are very few approaches for vascular analysis and classification of breast thermal images using interpretable vascular features. One major challenge is the automated detection of breast vascularity due to diffused vessel boundaries. We first propose a deep learning-based semantic segmentation approach that generates heatmaps of vessel structures from two-dimensional breast thermal images for quantitative assessment of breast vascularity. Second, we extract interpretable vascular parameters and propose a classifier to predict likelihood of breast cancer purely from the extracted vascular parameters. Results: The results of the cancer classifier were validated using an independent clinical dataset consisting of 258 participants. The results were encouraging as the proposed approach segmented vessels well and gave a good classification performance with area under receiver operating characteristic curve of 0.85 with the proposed vascularity parameters. Conclusions: The detected vasculature and its associated high classification performance show the utility of the proposed approach in interpretation of breast vascularity.

Preoperative breast magnetic resonance imaging in patients with ductal carcinoma in situ: a systematic review for the European Commission Initiative on Breast Cancer (ECIBC).

OBJECTIVE: To evaluate the impact of preoperative MRI in the management of Ductal carcinoma in situ (DCIS). METHODS: We searched the PubMed, EMBASE and Cochrane Library databases to identify randomised clinical trials (RCTs) or cohort studies assessing the impact of preoperative breast MRI in surgical outcomes, treatment change or loco-regional recurrence. We provided pooled estimates for odds ratios (OR), relative risks (RR) and proportions and assessed the certainty of the evidence using the GRADE approach. RESULTS: We included 3 RCTs and 23 observational cohorts, corresponding to 20,415 patients. For initial breast-conserving surgery (BCS), the RCTs showed that MRI may result in little to no difference (RR 0.95, 95% CI 0.90 to 1.00) (low certainty); observational studies showed that MRI may have no difference in the odds of re-operation after BCS (OR 0.96; 95% CI 0.36 to 2.61) (low certainty); and uncertain evidence from RCTs suggests little to no difference with respect to total mastectomy rate (RR 0.91; 95% CI 0.65 to 1.27) (very low certainty). We also found that MRI may change the initial treatment plans in 17% (95% CI 12 to 24%) of cases, but with little to no effect on locoregional recurrence (aHR = 1.18; 95% CI 0.79 to 1.76) (very low certainty). CONCLUSION: We found evidence of low to very low certainty which may suggest there is no improvement of surgical outcomes with pre-operative MRI assessment of women with DCIS lesions. There is a need for large rigorously conducted RCTs to evaluate the role of preoperative MRI in this population. KEY POINTS: • Evidence of low to very low certainty may suggest there is no improvement in surgical outcomes with pre-operative MRI. • There is a need for large rigorously conducted RCTs evaluating the role of preoperative MRI to improve treatment planning for DCIS.

Recommendations from the European Commission Initiative on Breast Cancer for multigene testing to guide the use of adjuvant chemotherapy in patients with early breast cancer, hormone receptor positive, HER-2 negative.

BACKGROUND: Predicting the risk of recurrence and response to chemotherapy in women with early breast cancer is crucial to optimise adjuvant treatment. Despite the common practice of using multigene tests to predict recurrence, existing recommendations are inconsistent. Our aim was to formulate healthcare recommendations for the question "Should multigene tests be used in women who have early invasive breast cancer, hormone receptor-positive, HER2-negative, to guide the use of adjuvant chemotherapy?" METHODS: The European Commission Initiative on Breast Cancer (ECIBC) Guidelines Development Group (GDG), a multidisciplinary guideline panel including experts and three patients, developed recommendations informed by systematic reviews of the evidence. Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence to Decision frameworks were used. Four multigene tests were evaluated: the 21-gene recurrence score (21-RS), the 70-gene signature (70-GS), the PAM50 risk of recurrence score (PAM50-RORS), and the 12-gene molecular score (12-MS). RESULTS: Five studies (2 marker-based design RCTs, two treatment interaction design RCTs and 1 pooled individual data analysis from observational studies) were included; no eligible studies on PAM50-RORS or 12-MS were identified and the GDG did not formulate recommendations for these tests. CONCLUSIONS: The ECIBC GDG suggests the use of the 21-RS for lymph node-negative women (conditional recommendation, very low certainty of evidence), recognising that benefits are probably larger in women at high risk of recurrence based on clinical characteristics. The ECIBC GDG suggests the use of the 70-GS for women at high clinical risk (conditional recommendation, low certainty of evidence), and recommends not using 70-GS in women at low clinical risk (strong recommendation, low certainty of evidence).

Retrospective analysis of the effect on interval cancer rate of adding an artificial intelligence algorithm to the reading process for two-dimensional full-field digital mammography.

Interval cancers are a commonly seen problem in organized breast cancer screening programs and their rate is measured for quality assurance. Artificial intelligence algorithms have been proposed to improve mammography sensitivity, in which case it is likely that the interval cancer rate would decrease and the quality of the screening system could be improved. Interval cancers from negative screening in 2011 and 2012 of one regional unit of the national German breast cancer screening program were classified by a group of radiologists, categorizing the screening digital mammography with diagnostic images as true interval, minimal signs, false negative and occult cancer. Screening mammograms were processed using a detection algorithm based on deep learning. Of the 29 cancer cases available, artificial intelligence identified eight out of nine of those classified as minimal signs, all six false negatives and none of the true interval and occult cancers. Sensitivity for lesions judged to be already present in screening mammogram was 93% (95% confidence interval 68-100) and sensitivity for any interval cancer was 48% (95% confidence interval 29-67). Using an artificial intelligence algorithm as an additional reading tool has the potential to reduce interval cancers. How and if this theoretical advantage can be reached without a negative effect on recall rate is a challenge for future research.

Retrospective comparison between single reading plus an artificial intelligence algorithm and two-view digital tomosynthesis with double reading in breast screening.

OBJECTIVE: To examine the breast cancer detection rate by single reading of an experienced radiologist supported by an artificial intelligence (AI) system, and compare with two-dimensional full-field digital mammography (2D-FFDM) double reading. MATERIALS AND METHODS: Images (3D-tomosynthesis) of 161 biopsy-proven cancers were re-read by the AI algorithm and compared to the results of first human reader, second human reader and consensus following double reading in screening. Detection was assessed in subgroups by tumour type, breast density and grade, and at two operating points, referred to as a lower and a higher sensitivity threshold. RESULTS: The AI algorithm method gave similar results to double-reading 2D-FFDM, and the detection rate was significantly higher compared to single-reading 2D-FFDM. At the lower sensitivity threshold, the algorithm was significantly more sensitive than reader A (97.5% vs. 89.4%, p = 0.02), non-significantly more sensitive than reader B (97.5% vs. 94.4%, p = 0.2) and non-significantly less sensitive than the consensus from double reading (97.5% vs. 99.4%, p = 0.2). At the higher sensitivity threshold, the algorithm was significantly more sensitive than reader A (99.4% vs. 89.4%, p < 0.001) and reader B (99.4% vs. 94.4%, p = 0.02) and identical to the consensus sensitivity (99.7% in both cases, p = 1.0). There were no significant differences in the detection capability of the AI system by tumour type, grading and density. CONCLUSION: In this proof of principle study, we show that sensitivity using single reading with a suitable AI algorithm is non-inferior to that of standard of care using 2D mammography with double reading, when tomosynthesis is the primary screening examination.

Stratification of Breast Cancer Risk in Terms of the Influence of Age and Mammographic density. / Stratifizierung des Brustkrebsrisikos hinsichtlich der Einflüsse von Alter und mammografischer Dichte.

PURPOSE: Analysis of the influence of the singular risk factors age and breast density on the 2-year incidence of breast cancer among participants in the German mammography screening program. MATERIALS AND METHODS: The multicenter study includes 111 456 subsequent round digital mammographic screening examinations from four screening units with prospective visual categorization of breast density. Based on detection in screening and during the 2-year interval after negative screening participation (interval cancers), 2-year breast cancer incidences (2 YBCI) (‰) were calculated in the 5-year age groups (5 YAG) of the target group 50-69 years and in the BI-RADS density categories ACR 1-4. Multivariate statistical evaluations were carried out using logistic regression models. RESULTS: With an increase in the 5 YAG, the 2 YBCI increased by 5.0 ‰, 6.7 ‰, 8.5 ‰ to 9.7 ‰, and was significantly different among 55-59, 60-64 and 65-69-year-old women compared to the youngest reference group 50-54 years (odds ratio (OR): 1.34; 1.68; and 1.93; p-value < 0.0001). With an increase in density categories 1-4, the 2 YBCI increased from 2.6 ‰, to 5.8 ‰, 9.6 ‰, and 9.7 ‰. The 2 YBCI differed significantly in breast density categories 2, 3, 4 from reference group 1 (OR: 2.17; 3.65; and 3.76; p-value < 0.0001). Only within the two main breast density groups 2 (frequency 44.3 %) and 3 (44.7 %), a significant increase in the 2 YBCI was observed across the 5 YAG (category 2: 3.7-8.9 ‰; category 3: 5.8-11.7 ‰; p-value < 0.001 each). The 2 YBCI was above the median of 7.5 ‰ in women with breast density category 2 and aged 65-69 years, as well as in women with breast density categories 3 and 4 aged 55-69 years. A 2 YBCI below the median was seen in women between 50-54 years regardless of breast density, as well as women in category 1 in all age groups. CONCLUSION: Within the main breast density categories 2 and 3 (almost 90 % of participants), incidences increase with age to double. A consistently low incidence is found regardless of breast density at a young screening age and in women with the lowest breast density. KEY POINTS: · The risk of breast cancer is modified by age in density categories.. · Women aged 50-54 years have a low risk in all density categories.. · Women in category ACR 1 of any age group have a low risk.. CITATION FORMAT: · Weigel S, Heindel W, Dietz C et al. Stratifizierung des Brustkrebsrisikos hinsichtlich der Einflüsse von Alter und mammografischer Dichte. Fortschr Röntgenstr 2020; 192: 678 - 685.