Pathology of breast cancer metastasis and a view of metastasis to the brain

Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis

This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC, and other metrics derived from confusion matrices. The CNN model showcased notable performance with an accuracy of 98% and an AUC of 0.97, effectively complementing the broader model analysis. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like "control at one year", "age of the patient", and "beam-on time for volume V1 treated" were consistently influential across various models, albeit their impacts were interpreted differently depending on the model's underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial. Our research explores the effects of staged Gamma Knife radiosurgery (GKRS) on larger tumors, revealing no significant outcome differences across protocols. It uniquely considers the impact of beam-on time and fraction intervals on treatment efficacy. However, the investigation is limited by a small patient cohort and data from a single institution, suggesting the need for future multicenter research.

The pathologic diagnosis of carcinoma in various tissues

Carcinoma is defined as cancer arising from the epithelial cells that line an organ or tissue. The most common carcinoma in males arises in the prostate and breast in females; while the most significant cause of cancer related mortality in the United States is carcinoma of the lung. Cancers typically begin as a clonal proliferation of cells that have acquired distinct mutations, which then progress to invasive carcinoma as the cells breach the underlying basement membrane associated with the tissue of origin. This transition to invasive carcinoma carries with it the potential to invade blood vessels or lymphatic channels and metastasize to lymph nodes or distant tissues resulting in increased morbidity and mortality. The histologic diagnosis of carcinoma is rendered based on both the cytologic and architectural features of the tumor, as well as the location of the proliferating cells and the interaction with the surrounding stromal elements.