Department of Biochemistry

Background/Objectives: Colorectal cancer (CRC) with hepatic (CRLM) and pulmonary metastases (CRLU) presents a significant clinical challenge, leading to poor prognosis. Surgical resection of these metastases remains controversial because of limited evidence supporting its long-term benefits. To evaluate the impact of surgical resection of both hepatic and pulmonary metastases on long-term survival in patients with multivisceral metastatic colorectal cancer, this retrospective cohort study included 192 patients with UICC stage IV CRC treated at a high-volume academic center. Methods: Patients were divided into two groups: those who underwent surgical resection of both hepatic and pulmonary metastases (n = 100) and those who received non-surgical treatment (n = 92). Propensity score matching was used to adjust for baseline differences. The primary outcome was overall survival (OS). Results: Unadjusted analysis showed a significant OS benefit in the surgical group (median OS: 6.97 years) compared with the conservative group (median OS: 2.17 years). After propensity score matching, this survival advantage persisted (median OS: 5.58 years vs. 2.35 years; HR: 0.3, 95% CI: 0.18–0.47, p < 0.0001). Conclusions: Surgical resection of hepatic and pulmonary metastases in multivisceral metastatic CRC significantly improves long-term survival, supporting an aggressive surgical approach in selected patients.

Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.