Citations

Objectives: Women with a history of breast cancer are at increased risk of developing subsequent breast cancer, including ipsilateral recurrence and contralateral new primary breast cancer. This study evaluated the discriminatory performance of a mammogram-based artificial intelligence (AI) risk model for predicting subsequent breast cancer within one year after a negative screening mammogram. Methods: This enriched retrospective case–control study included women with a prior history of breast cancer who underwent screening digital breast tomosynthesis between January 2018 and December 2023 at three affiliated academic breast imaging centers. Digital breast tomosynthesis examinations classified as BI-RADS 1 or 2 were retrospectively analyzed using the ProFound AI® Risk model version 1.0 to estimate 1-year breast cancer risk. Patients were classified according to whether they developed subsequent breast cancer within one year of the index screening examination. Model discrimination was evaluated using receiver operating characteristic analysis. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated at an exploratory cutoff selected by maximizing the Youden index. Results: The study included 96 women (mean age, 65.3 ± 8.7 years), of whom 32 developed subsequent breast cancer within one year, and 64 did not. The mean AI risk score was significantly higher in the subsequent breast cancer group than in the control group (1.18 ± 0.59 vs. 0.49 ± 0.41; p < 0.001). The AI model demonstrated an AUC of 0.824 (95% CI: 0.728–0.921). At an exploratory cutoff of 0.39, sensitivity was 81.3%, specificity was 76.6%, PPV was 63.4%, and NPV was 89.1%. In separate exploratory analyses, the AUC was 0.790 (95% CI: 0.641–0.939) for ipsilateral recurrence and 0.860 (95% CI: 0.752–0.974) for contralateral new primary breast cancer. AI risk scores were not significantly correlated with tumor size or age at subsequent breast cancer diagnosis. Conclusions: In this enriched retrospective case–control study, higher mammogram-based AI risk scores were associated with subsequent breast cancer within one year after a negative screening examination. The model demonstrated discriminatory performance for both ipsilateral recurrence and contralateral new primary breast cancer; however, these analyses were exploratory. Because the cohort was enriched for subsequent breast cancer events, the reported predictive values are specific to the study sample and should not be extrapolated to routine surveillance populations. Larger prospective cohorts are needed to validate discrimination, calibration, and clinical utility.

Abstract Objective Current clinical risk models were developed for longer-term breast cancer risk stratification and therefore have limited utility for predicting short-term (1-year) breast cancer risk. This study evaluates the performance of a mammogram-based artificial intelligence (MB-AI) risk model for predicting breast cancer diagnosis within 1 year in high-risk women. Methods This retrospective case-control study included 340 women who underwent supplemental MRI screening within 1 year following a negative mammogram, of whom 101 (29.7%) developed breast cancer and 239 (70.3%) remained cancer-free during the same interval. Artificial-intelligence–assigned absolute 1-year risk scores, Tyrer–Cuzick (TC) lifetime and 10-year scores, and Gail lifetime and 5-year scores were analyzed. Model discrimination was evaluated using receiver operating characteristic (ROC) curves. Sensitivity, specificity, and predictive values were estimated at optimal cutoffs. Subgroup analyses examined model performance across genetic, menopausal, reproductive, and tumor-based strata. Results The MB-AI model demonstrated the highest overall discriminative ability (area under the curve [AUC] = 0.812), with strong performance in extremely dense breasts (AUC = 0.976). By comparison, TC lifetime (AUC = 0.625) and Gail lifetime (AUC = 0.668) models showed modest discrimination, while TC 10-year and Gail 5-year models did not discriminate (AUCs ≤0.49). Subgroup analyses showed consistent performance across clinical and pathological categories. The MB-AI model also showed an AUC comparable to that of previously developed AI models. Conclusions A mammogram-based AI model demonstrated improved performance compared with traditional clinical risk models for predicting breast cancer diagnosis within 1 year in women undergoing supplemental screening. These findings support that AI-based approaches may reflect both short-term risk and early imaging features of occult disease and suggest further investigation of AI as a complement to existing risk stratification strategies.

Objectives Heritable connective tissue disorders (HCTDs), including hypermobile Ehlers-Danlos syndrome (hEDS) and hypermobile spectrum disorder (HSD), have complex clinical manifestations, but the prevalence of left common iliac vein (LCIV) compression in these groups is not well known. This study aimed to compare the prevalence of significant LCIV compression in patients with hEDS, HSD, and healthy controls. Methods This retrospective, IRB-approved, single-center study analyzed 109 patients (25 hEDS, 64 HSD, 20 controls) who underwent contrast-enhanced abdominal and pelvic CT imaging from 2020 to 2023. LCIV compression was measured on axial CT, with >50% luminal narrowing defined as significant. Statistical analyses included chi-square tests, odds ratios, and t -tests. Interrater reliability was evaluated using intraclass correlation coefficients. Results Significant LCIV compression occurred in 68.0% of hEDS, 65.6% of HSD, and 35.0% of controls ( p = 0.034). Both hEDS ( p = 0.027) and HSD ( p = 0.015) groups had significantly higher compression rates than controls, with no statistically significant difference between the two clinical groups ( p = 0.831). Odds of compression were nearly fourfold greater in hEDS (OR 3.95) and HSD (OR 3.54) compared to controls. Conclusion LCIV compression is significantly more prevalent in patients with hEDS and HSD than in healthy controls, warranting further investigation of its clinical significance.

Abstract May-Thurner physiology (MTP) can lead to various congestion syndromes due to compression of the left common iliac vein (LCIV) by the right common iliac artery (RCIA). This compression may result in venous reflux through the lumbar vein, leading to congestion of the spinal epidural venous plexus (EVP), which could contribute to refractory headaches. This case report details the clinical course of a patient with severe refractory new daily persistent headache associated with MTP who underwent ascending lumbar vein (ALV) embolization. The patient is a 59-year-old female with a 3-year history of daily persistent headache which failed multiple migraine prevention therapies and minimally invasive procedures. Imaging studies revealed significant LCIV compression by the RCIA, retrograde ALV flow, and EVP congestion. The patient underwent ALV embolization, resulting in significant symptomatic relief. At the 22-month follow-up, headache severity reduced by 80%, with the patient no longer requiring routine headache medications. Additionally, treatment of associated pelvic congestion syndrome through gonadal vein embolization resolved longstanding associated pelvic pain and pressure. This case highlights the role of venous congestion in refractory headache syndromes and underscores the potential of targeted venous interventions, such as embolization, in their management. The findings expand on emerging evidence linking venous compression syndromes to headache pathophysiology and support exploring interventional strategies as viable treatment options for selected patients. Further research is needed to validate these findings and establish evidence-based guidelines for clinical practice.