Cyclin‐dependent kinase 4/6 (CDK4/6) inhibitors have become a cornerstone in the first‐line management of hormone receptor‐positive, human epidermal growth factor receptor 2‐negative (HR+/HER2−) advanced breast cancer. Despite their clinical efficacy, most patients eventually experience disease progression, and optimal treatment strategies following CDK4/6 inhibitor resistance remain unclear. Emerging evidence suggests that switching endocrine therapy (ET) while continuing cyclin inhibition may provide additional clinical benefit. Dalpiciclib, a selective CDK4/6 inhibitor, in combination with physician‐selected ET, represents a potential option in this setting. Concurrently, 16α‐[¹⁸F]fluoro‐17β‐estradiol ([¹⁸F]FES) PET/CT, a novel imaging modality targeting estrogen receptors (ER), enables non‐invasive, whole‐body assessment of ER expression in metastatic lesions, offering a personalized approach to treatment selection.

This is a prospective, single‐center, single‐arm Phase Ⅱ clinical trial evaluating the efficacy and safety of dalpiciclib plus ET in HR+/HER2− advanced breast cancer patients who progressed on prior CDK4/6 inhibitor therapy. Forty eligible patients with confirmed metastases and at least one [¹⁸F]FES‐positive lesion will be enrolled. Participants will receive dalpiciclib combined with ET as determined by the treating physician. The primary endpoint is progression‐free survival (PFS). Secondary endpoints include objective response rate (ORR), disease control rate (DCR), and overall survival (OS).

Beyond evaluating efficacy, this single‐center phase Ⅱ trial will provide practical insight into the feasibility of [¹⁸F]FES PET/CT‐guided patient selection, including standardized imaging workflows and multidisciplinary implementation in routine clinical research. Positive outcomes may support a personalized, imaging‐guided strategy to prolong disease control after prior CDK4/6 inhibitor progression.

Approved by the Ethics Committee of Peking Union Medical College Hospital (Approval number: K3629); registered at ClinicalTrials. gov (NCT05613270).

Sclerosing adenosis (SA) and breast cancer (BC) often exhibit overlapping clinical, imaging, and pathological characteristics, making them difficult to differentiate. SA may also coexist with BC (SA + BC), including ductal carcinoma in situ (SA‐DCIS) and invasive breast cancer (SA‐IBC), which complicates diagnosis even when core‐needle biopsy (CNB) suggests SA. This study aimed to develop interpretable AI‐based binary and ternary classification models that leverage clinical and imaging features to distinguish SA‐only from SA + BC and to further differentiate among SA‐only, SA‐DCIS, and SA‐IBC.

We retrospectively analyzed a cohort of 726 patients with SA (January 2006 to December 2021), comprising 537 SA‐only and 189 SA + BC cases (90 SA‐DCIS, 99 SA‐IBC). Multiple machine learning algorithms—logistic regression, support vector machine, decision tree, XGBoost, and random forest—were compared using AUC, accuracy, F1‐score, and C‐index. Model interpretability was assessed with SHAP to elucidate feature contributions and identify key predictors. Additionally, we incorporated an independent external validation cohort consisting of 113 patients to verify the model's effectiveness.

XGBoost consistently outperformed other algorithms in both tasks. Eight features emerged as most informative: age, ultrasound BI‐RADS category, maximum and minimum ultrasound diameters, ultrasound margin characteristics, biopsy procedure, mammographic density, and microcalcifications. For binary classification (SA‐only vs. SA + BC), XGBoost achieved an AUC of 0.925, accuracy of 0.883, and C‐index of 0.844. For ternary classification (SA‐only, SA‐DCIS, SA‐IBC), the model achieved an AUC of 0.888, accuracy of 0.811, and C‐index of 0.813. Age, ultrasound BI‐RADS, and minimum lesion diameter were consistently top predictors. We further proposed a three‐tier interpretability framework (global, cohort‐level; local, subgroup‐level; and individual, case‐level) to facilitate clinical translation.

Given the substantial risk of coexisting of SA with DCIS or IBC, and the potential for CNB to underestimate disease due to limited sampling, lesions diagnosed as SA on CNB should be evaluated with additional modalities before determining the need for surgical excision. The proposed interpretable AI model enhances discrimination between SA‐only and SA with concomitant breast cancer (SA + BC), thereby supporting more informed clinical decision‐making in breast disease management.