Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology

Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

Sarcopenia in Breast Cancer Patients: A Systematic Review and Meta-Analysis

(1) Background: We estimated the prevalence and clinical outcomes of sarcopenia among breast cancer patients. (2) Methods: A systematic literature search was carried out for the period between July 2023 and October 2023. Studies with breast cancer patients evaluated for sarcopenia in relation to overall survival (OS), progression-free survival (PFS), relapse of disease (DFS), pathological complete response (pCR), or toxicity to chemotherapy were included. (3) Results: Out of 359 screened studies, 16 were eligible for meta-analysis, including 6130 patients, of whom 5284 with non-MBC. Sarcopenia was evaluated with the computed tomography (CT) scan skeletal muscle index and, in two studies, with the dual-energy x-ray absorptiometry (DEXA) appendicular lean mass index. Using different classifications and cut-off points, overall, there were 2007 sarcopenic patients (33%), of whom 1901 (95%) presented with non-MBC. Sarcopenia was associated with a 33% and 29% higher risk of mortality and progression/relapse of disease, respectively. Sarcopenic patients were more likely to develop grade 3-4 toxicity (OR 3.58, 95% CI 2.11-6.06, p < 0.0001). In the neoadjuvant setting, a higher rate of pCR was observed among sarcopenic patients (49%) (OR 2.74, 95% CI 0.92-8.22). (4) Conclusions: Our meta-analysis confirms the correlation between sarcopenia and negative outcomes, especially in terms of higher toxicity.

Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation

BACKGROUND

Sarcopenia is a well know prognostic factor in oncology, influencing patients' quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes.

METHODS

We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints.

RESULTS

97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10-20% up to ~45-55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle.

CONCLUSIONS

A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes.

An Overview on the Impact of Microbiota on Malaria Transmission and Severity: Plasmodium-Vector-Host Axis

PURPOSE

Malaria, which is a vector-borne disease caused by Plasmodium sp., continue to become a serious threat, causing more than 600,000 deaths annually, especially in developing countries. Due to the lack of a long-term, and effective vaccine, and an increasing resistance to antimalarials, new strategies are needed for prevention and treatment of malaria. Recently, the impact of microbiota on development and transmission of Plasmodium, and the severity of malaria has only begun to emerge, although its contribution to homeostasis and a wide variety of disorders is well-understood. Further evidence has shown that microbiota of both mosquito and human host play important roles in transmission, progression, and clearance of Plasmodium infection. Furthermore, Plasmodium can cause significant alterations in the host and mosquito gut microbiota, affecting the clinical outcome of malaria.

METHODOLOGY

In this review, we attempt to summarize results from published studies on the influence of the host microbiota on the outcome of Plasmodium infections in both arthropods and mammalian hosts.

CONCLUSION

Modifications of microbiota may be an important potential strategy in blocking Plasmodium transmission in vectors and in the diagnosis, treatment, and prevention of malaria in humans in the future.