Aim: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.Materials and methods: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD.Results: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD.Conclusion: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.

To investigate whether artificial-intelligence-based, computer-aided diagnosis (AI-CAD) could facilitate the detection of missed cancer on digital mammography, a total of 204 women diagnosed with breast cancer with diagnostic (present) and prior mammograms between 2018 and 2020 were included in this study. Two breast radiologists reviewed the mammographic features and classified them into true negative, minimal sign or missed cancer. They analyzed the AI-CAD results with an abnormality score and assessed whether the AI-CAD correctly localized the known cancer sites. Of the 204 cases, 137 were classified as true negative, 33 as minimal signs, and 34 as missed cancer. The sensitivity, specificity and diagnostic accuracy of AI-CAD were 84.7%, 91.5% and 86.3% on diagnostic mammogram and 67.2%, 91.2% and 83.38% on prior mammogram, respectively. The AI-CAD correctly localized 27 cases from 34 missed cancers on prior mammograms. The findings in the preceding mammography of AI-CAD-detected missed cancer were common in the order of calcifications, focal asymmetry and asymmetry. Asymmetry was the most common finding among the seven cases, which could not be detected by AI-CAD in the missed cases (5/7). The assistance of AI-CAD can be helpful in the early detection of breast cancer in mammography screenings.

Breast cancer remains a major global health challenge¹. Here, to comprehensively characterize its genomic landscape and the clinical significance of genomic characteristics, we analysed whole-genome sequences from 1,364 clinically annotated breast cancers, with transcriptome data available for most cases. Our study expands the repertoire of oncogenic alterations and identifies novel driver genes, recurrent gene fusions, structural variants and copy number alterations. Timing analyses on copy number alterations suggest that genomic instability emerges decades before tumour diagnosis, and offer insights into early initiation of tumorigenesis. Pattern-driven genomic features, including mutational signatures², homologous recombination deficiency³, tumour mutational burden and tumour heterogeneity scores⁴, were associated with clinical outcomes, highlighting their potential utility as predictive biomarkers for clinical evaluation of treatments such as CDK4/6 and HER2 inhibitors, as well as adjuvant and neoadjuvant chemotherapy. These findings highlight the power of large-scale, clinically annotated whole-genome sequencing in advancing our understanding of how genomic alterations shape patient outcomes.

To investigate the imaging characteristics of early and late recurrent breast cancer and the detectability of mammography, ultrasonography, and breast magnetic resonance imaging (MRI) in patients who underwent breast-conservation therapy (BCT). Total of 1312 women with 2026 surveillance breast MRI after BCT between January 2014 and September 2018 were studied. Early recurrence was defined as newly diagnosed breast cancer and/or axillary metastasis within 12 months of surgery. Late recurrence was defined as recurrence after 12months of surgery. We assessed the detectability of recurrent lesions in each postoperative imaging modality and evaluated characteristics of recurrent lesions on postoperative MRI by comparing early and late recurrence groups. Of the 2026 cases, 103 were confirmed as recurrent breast cancer by biopsy or surgery. Thirty-one cases were early recurrence, and 72 cases were late recurrence. MRI showed significantly higher detectability for recurrent lesions (102 cases, 99%) than mammography (59.4%, p < 0.001) or ultrasound (68.9%, p < 0.001), or both mammography and ultrasound (81.6%, p < 0.001). The recurrent lesions did not have typical malignant morphologic features, but variable features on MRI. However, early recurrent lesions showed fast enhancement in early dynamic phase regardless of the kinetic pattern of delayed dynamic phase; and late recurrence lesions showed early fast enhancement and delayed washout pattern. There were 19 cases which were not detected on mammography or ultrasound but could only be detected with MRI. Postoperative breast MRI showed significantly higher detectability for recurrent lesions than mammography and ultrasound. Early fast enhancement is the most important feature of recurrent lesions on postoperative breast MRI for both early and late recurrence groups. Due to its high possibility of recurrence, further work-up should be considered regardless of their morphologic features.

The purpose of this study is to investigate the usefulness of magnetic resonance imaging (MRI) for the detection of local recurrence after nipple-sparing mastectomy (NSM) or skin-sparing mastectomy (SSM) with immediate reconstructive surgery for breast cancer. Two hundred and eighty-six NSM or SSM procedures and immediate reconstruction cases between August 2015 and February 2020 were reviewed. The detectability rates of for local recurrence using MRI and ultrasound were assessed, and the characteristics of recurrent and primary cancers were evaluated. The patients with multifocal or multicentric primary cancer and a dense parenchymal pattern showed a higher recurrence rate (p < 0.001). A total of 22 cases showed recurrence, and due to multifocal recurrence, a total of 27 recurrent lesions were identified in the reconstructed breast, of which 12 were symptomatic and 15 were asymptomatic (p < 0.001). With the exception of skin recurrence (n = 6), MRI showed a significantly higher detectability rate (95.2%, 20 of 21) than ultrasound (38.1%, 8 of 21) for the recurrence of cancer in the reconstructed breast (p < 0.001), especially for small-sized (<1 cm) asymptomatic lesions. In addition, the mean recurrence interval of MRI-detected asymptomatic lesions was 21.7 months (SD ± 17.7), which was significantly longer than that of symptomatic recurrence. In conclusion, postoperative MRI can be useful for identifying small-sized (<1 cm) asymptomatic recurrence lesions in reconstructed breast tissue after NSM or SSM, which can be implemented within two years of surgery.

This study aimed to predict early breast cancer recurrence in women under 40 years of age using radiomics signature and clinicopathological information. We retrospectively investigated 155 patients under 40 years of age with invasive breast cancer who underwent MRI and surgery. Through stratified random sampling, 111 patients were assigned as the training set, and 44 were assigned as the validation set. Recurrence-associated factors were investigated based on recurrence within 5 years during the total follow-up period. A Rad-score was generated through texture analysis (3D slicer, ver. 4.8.0) of breast MRI using the least absolute shrinkage and selection operator Cox regression model. The Rad-score showed a significant association with disease-free survival (DFS) in the training set (p = 0.003) and validation set (p = 0.020) in the Kaplan–Meier analysis. The nomogram was generated through Cox proportional hazards models, and its predictive ability was validated. The nomogram included the Rad-score and estrogen receptor negativity as predictive factors and showed fair DFS predictive ability in both the training and validation sets (C-index 0.63, 95% CI 0.45–0.79). In conclusion, the Rad-score can predict the disease recurrence of invasive breast cancer in women under 40 years of age, and the Rad-score-based nomogram showed reasonably high DFS predictive ability, especially within 2 years of surgery.