Keyword: Monitoring

ABSTRACT: Cancer behaves as a dynamic evolutionary system, whereas conventional tissue biopsy and imaging provide incomplete temporal information about how a tumour is changing. This review critically examines whether longitudinal liquid-biopsy measurement, combined with artificial intelligence (AI), can move oncology from tumour monitoring toward genuine prediction of cancer evolution and treatment resistance. We synthesise mechanistic, clinical, and computational evidence on circulating tumour DNA, circulating tumour cells, extracellular vesicles, and multimodal liquid-biopsy platforms, together with AI approaches applied to longitudinal cancer data, drawing on landmark and recent studies across colorectal, lung, breast, and ovarian cancer. Current evidence distinguishes three increasingly demanding tasks: early detection, longitudinal monitoring, and genuine forecasting. Longitudinal circulating tumour DNA analysis can detect molecular progression and emerging resistance mutations months before radiographic progression, and machine-learning models can stratify survival using serial biomarker metrics; however, most published "AI prediction" studies demonstrate earlier detection or prognostic association rather than prospectively validated forecasting of a future, patient-specific event. Biological variability in tumour shedding, analytical noise, small and irregularly sampled longitudinal datasets, overfitting, data leakage, weak external validation, and limited interpretability constrain the transition from detection to actionable prediction. AI-enabled liquid biopsy has genuine potential to forecast tumour trajectories, but current evidence is considerably stronger for molecular monitoring and early detection than for prospectively validated, clinically actionable prediction. Realising this potential will require standardised assays, prospective longitudinal cohorts with predefined prediction horizons, rigorous external and temporal validation, calibrated and interpretable models, and interventional trials linking predictions to treatment decisions before AI-enabled liquid biopsy can be considered ready to guide clinical care.

ABSTRACT: Objective: To evaluate the medical-organizational and clinical effectiveness of optimizing comprehensive diagnostics and follow-up care for patients with cervical intraepithelial neoplasia (CIN) through the introduction of a specialized digital module into the national Health Management Information System (HMIS/DHIS2) of the Republic of Tajikistan.
Materials and Methods: An analysis was conducted on the results of diagnosis, treatment, and dynamic follow-up of 542 patients with morphologically verified CIN1–3/Tis, aged 20 to 65 years, examined during 2023–2026. The main group using the digital system (n=82) was compared with a control group managed through paper-based documentation (n=460). The developed Digital Module for Early Diagnosis (DMED) of cervical cancer enabled the integration of 51 specialized cervical pathology units at district and city health centers, HPV diagnostics laboratories, cytology laboratories, and the State Institution "Republican Oncological Scientific Center" (ROSC). Time intervals for completing stages of medical care and the proportion of patients under follow-up care were assessed.
Results: The use of automatic tracking algorithms through the DMED for cervical cancer reduced the total diagnostic and treatment time interval (from abnormal visual screening through extended diagnostics to organ-preserving treatment with LEEP/cone biopsy) from 68.4 ± 5.2 to 21.2 ± 2.4 days (p < 0.001). The proportion of women lost to follow-up decreased from 34.2% to 6.1%. All 5 cases of invasive cervical cancer detected using the DMED were diagnosed at stage I of the disease.
Conclusion: The implementation of the DMED for cervical cancer optimizes the organization of gynecologic oncology care, eliminates lost links in patient referral pathways, and ensures a high level of monitoring of the health status of patients with precancerous pathology of the cervix.

ABSTRACT: Background: A multidisciplinary approach is an important component of high-quality cancer care, enabling collaborative decision-making on diagnostic and treatment strategies in accordance with clinical protocols and recommendations of international oncology societies. In accordance with the national Standard for the Organization of Cancer Care in the Republic of Kazakhstan, multidisciplinary teams (MDTs) covering the main areas of oncology have been established at the State Enterprise on the Right of Economic Management “Multidisciplinary Medical Center” of the Akimat of Astana.
Objective: To evaluate the organization of multidisciplinary team activities and their role in collaborative clinical decision-making in cancer care.
Materials and Methods: Six specialized MDTs were established: thoracic surgery, abdominal surgery, head and neck oncology, gynecologic oncology, urologic oncology, and medical oncology, along with a chemotherapy council. MDT meetings review newly diagnosed patients with histologically verified malignancies, patients with diagnostic challenges, recurrent or progressive disease, treatment-related complications or contraindications, and cases requiring modification of the treatment strategy. Decisions are made collaboratively based on medical records, diagnostic findings, clinical protocols, and recommendations of the European Society for Medical Oncology (ESMO), American Society of Clinical Oncology (ASCO), and National Comprehensive Cancer Network (NCCN). MDT protocols are continuously monitored for compliance with established requirements.
Results: In 2025, a total of 4,142 patient cases were reviewed at MDT meetings. The MDT process included collaborative selection of diagnostic and treatment strategies, determination of further patient management, assessment of the need to modify previously prescribed treatment, referral for specialized and highly specialized medical care, and evaluation of indications for targeted therapy. MDT decisions were documented in meeting protocols and incorporated into patients’ medical records. The established MDT process ensured systematic multidisciplinary review of clinical cases and continuous monitoring of clinical decisions.
Conclusions: The implementation of specialized multidisciplinary teams provides a structured framework for collaborative decision-making in cancer care and promotes a consistent approach to diagnostic and treatment strategies. Regular monitoring of MDT protocols supports compliance with established requirements. Further assessment using quantitative indicators of adherence to MDT recommendations, treatment modifications, and patient outcomes is warranted to objectively evaluate the impact of MDTs on the quality and outcomes of cancer care.