Results for “Biopsy” — Page 2

PubMed2026

Breastfeeding Outcomes After Radiofrequency Ablation and Surgical Excision of Fibroadenomas Compared to Natural History: A Retrospective Cohort Study.

BACKGROUND: Fibroadenomas, common benign breast tumors in women of reproductive age, are increasingly managed with minimally invasive radiofrequency ablation (RFA) or surgical excision. However, their impact on breastfeeding outcomes remains underexplored. We aimed to assess breastfeeding ability and breast tissue changes in women treated with RFA or surgery compared to those with untreated fibroadenomas. METHODS: In this retrospective cohort study, we evaluated 153 women with biopsy-confirmed fibroadenomas across five groups: RFA with prior breastfeeding (RFA-PreBF; n = 26), RFA with posttreatment breastfeeding (RFA-PostBF; n = 22), surgical excision with prior breastfeeding (Surgical-PreBF; n = 30), surgical excision with posttreatment breastfeeding (Surgical-PostBF; n = 20), and noninterventional with breastfeeding (Observation-BF; n = 55). Breastfeeding ability was assessed using a 5-point scale, and breast tissue changes were evaluated via ultrasound at 18 months, and all breastfeeding attempts occurred at a minimum of 12 months postprocedure. Fisher's exact test compared complete lactation failure rates, and a power analysis validated the study design (Fig. 1). FINDINGS: The combined RFA group (n = 48) had a higher complete lactation failure rate (14.6%) than the Observation-BF group (3.6%; Fisher's exact test, p = 0.045). Similarly, the combined surgical group (n = 50) had a higher complete lactation failure rate (16.0%) than the Observation-BF group (p = 0.034). When pooled, the combined intervention group (n = 98) showed a complete lactation failure rate of 15.3% versus 3.6% in the Observation-BF group. Residual tissue correlated with lactation impairment in the RFA group (Spearman's ρ, p < 0.05). Ultrasound showed no tumor recurrence, with cystic changes in some cases not linked to breastfeeding impairment. Power analysis confirmed 80% power to detect a medium effect size (f = 0.25, Fig. 1), supporting the study's robustness.7 RFA-PreBF (n = 26), RFA-PostBF (n = 22), Surgical-PreBF (n = 30), Surgical-PostBF (n = 20), and Observation-BF (n = 55). Breastfeeding ability was assessed using a 5-point scale, and breast tissue changes were evaluated via ultrasound at 18 months. Fisher's exact test compared complete lactation failure rates, and a power analysis validated the study design (Fig. 1).[Figure: see text]Interpretation:RFA and surgical excision are effective for fibroadenoma management but increase the risk of complete lactation failure compared to untreated fibroadenomas. Careful patient selection, precise procedural techniques, and vigilant posttreatment monitoring are essential to optimize breastfeeding outcomes, particularly for women planning future pregnancies.

Humans↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗