'Tis but a scratch: a critical review of the Women's Health Initiative evidence associating menopausal hormone therapy with the risk of breast cancer

Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach

BACKGROUND

Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients.

METHODS

A retrospective analysis was conducted on 102 patients with invasive breast cancer confirmed by surgical pathology. Using the MR750 3.0 T as the examination device, the subject underwent the examination in standard breast positions and sequences. Diffusion-weighted imaging (DWI) was performed with two selected b-values, specifically 0 and 1000 s/mm2. Following the injection of the contrast agent, dynamic scans were conducted across six phases, and delayed phase sagittal images were acquired using the VIBRANT sequence. Texture features were extracted from MRI images, and key radiomic and clinical features were selected using variance thresholding, correlation filtering, and logistic regression. A predictive model was developed combining these features, and its performance was evaluated through sensitivity, specificity, and area under the curve (AUC) metrics.

RESULTS

The univariate models based on individual MRI sequences or clinical data demonstrated variable diagnostic performance. In contrast, the multifactorial model that combined radiomic features with clinical data achieved significantly higher accuracy, with an AUC of 0.829, sensitivity of 76.9 %, and specificity of 83.3 %.

CONCLUSION

Integrating MRI radiomics and clinical data enhances the preoperative prediction of vascular invasion in breast cancer. This approach can improve diagnostic accuracy, providing valuable insights for clinical decision-making and personalized treatment strategies.

Systemic hormone therapy after breast and gynecological cancers: an Italian expert group consensus opinion

The specific Italian Group of Study of the Menopause formulated a consensus opinion on the use of estrogen therapy (ET) or combined estro-progestin hormone therapy (HT) after breast and gynecological cancers. This consensus is based on the risk of recurrence of the specific cancer during ET/HT, the presence of steroid receptors in cancer cells, the use of adjuvant hormone therapies and data on the use of ET/HT after cancer. The following positions were reached. ET/HT can be used after vulvar cancers and melanoma, but with great caution after the rare adenocarcinomas. ET/HT can be used after cervical cancer, but ET should be used with caution after adenocarcinomas. ET/HT can be used after International Federation of Obstetrics and Gynecology (FIGO) stage I-II estrogen-dependent endometrial cancers, except in Black women, and can probably be used after estrogen-independent endometrial cancers. ET/HT cannot be administered or should be used with great caution after most uterine sarcomas. ET/HT can probably be used after ovarian neoplasms except for granulosa cell tumors, and with great caution after low-grade serous ovarian carcinoma and serous borderline ovarian tumors. ET/HT can be used with great caution in women after estrogen receptor (ER)/progesterone receptor (PR)-positive breast cancer and is probably allowed after ER/PR-negative breast cancer.