Keyword: Oncology

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: Background: The rapid population growth of Astana has been accompanied by an increasing demand for specialized cancer care. Cancer prevention, early diagnosis, improvement of treatment strategies, and enhancement of patients’ quality of life and survival remain key priorities of the oncology service.
Objective: To assess major changes in the organization, capacity, and technological development of the oncology service in Astana over a 15-year period.
Materials and Methods: A retrospective analysis of the main organizational indicators of the State Enterprise on the Right of Economic Management “Multidisciplinary Medical Center” of the Akimat of Astana over a 15-year period was performed. Changes in patient volume, staffing, implementation of modern diagnostic and treatment technologies, screening, and cancer prevention activities were evaluated.
Results: Over 15 years, the nearly threefold increase in the population of Astana was accompanied by a substantial rise in the workload of the oncology service. Despite a planned outpatient capacity of 380 visits per day, the actual number reached up to 1,300 patients daily. The total number of employees increased from 250 to 864, including 177 physicians and 318 nursing professionals. Modern laboratory and radiation diagnostic methods, immunohistochemical and cytochemical testing, targeted therapy, and immunotherapy were introduced into clinical practice. Linear accelerators, angiographic systems, and video endoscopic equipment expanded the capabilities of radiation therapy, diagnostic procedures, and minimally invasive treatment. Cancer screening, follow-up programs, and public cancer awareness initiatives were further developed to support early detection of malignancies.
Conclusions: Over the 15-year period, the oncology service in Astana substantially expanded its workforce and technological capacity while responding to rapid population growth and increasing demand for cancer care. The implementation of modern diagnostic and treatment technologies, development of screening programs, and strengthening of human resources provide a foundation for further improvements in the accessibility and quality of oncology care.

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.

ABSTRACT: Introduction: The growing cancer burden in Kazakhstan necessitates improvement not only in antitumor treatment but also in comprehensive patient-centered support. According to the analyzed statistical data, the number of patients under follow-up observation increased from 190,159 in 2020 to 246,205 in 2025, corresponding to a 29.5% increase. During this period, the number of registered breast cancer cases increased by 31.8%, and lung cancer by 64.9%. Cancer and its treatment can be accompanied by anxiety, emotional distress, and reduced quality of life. Photodynamic therapy (PDT) is a modern minimally invasive treatment for a range of oncological diseases; however, the psychological characteristics of patients undergoing this method and the effectiveness of specialized support remain insufficiently studied.
Objective: To analyze the dynamics of the cancer burden in Kazakhstan for 2020–2025 and to summarize data on the role of psycho-oncological support for patients undergoing PDT, while identifying prospects for further study in this area.
Materials and Methods: An analytical analysis of secondary data was conducted in two stages. At the first stage, a retrospective analysis of cancer incidence indicators in Kazakhstan for 2020–2025 was performed, calculating the absolute and relative change in the number of patients under follow-up observation, as well as indicators for breast cancer and lung cancer. At the second stage, a qualitative analysis of published scientific data on psycho-oncology, psychological distress, anxiety, quality of life, and psychological support for cancer patients, including materials on PDT, was conducted. Primary collection of clinical and psychometric data was not performed.
Results: During the study period, the number of patients under follow-up observation increased by 56,046 individuals (29.5%)—from 190,159 to 246,205. The number of breast cancer cases increased from 4,290 to 5,652 (31.8%), and lung cancer from 2,324 to 3,833 (64.9%). A literature analysis showed that cancer and antitumor treatment can be accompanied by emotional distress, anxiety, and reduced quality of life. Psycho-oncological interventions generally demonstrate a positive impact on the psychological state and quality of life of patients. In PDT, psychological tension may be related to the diagnosis, anticipation of the procedure, and treatment characteristics; however, the specific effectiveness of psychological support in this method remains insufficiently studied.
Conclusions: The growing cancer burden in Kazakhstan confirms the need to develop a comprehensive patient support system. Psycho-oncological support is regarded as a promising component of care in PDT; however, prospective clinical studies using validated psychometric instruments to assess anxiety, depressive symptoms, psychological distress, and quality of life before and after treatment are needed to confirm its effectiveness.

ABSTRACT: Introduction: Fetal brain tumors account for 2% of pediatric tumors and 10% of all congenital tumors. At the same time, prenatal diagnosis of neuro-oncological pathology by ultrasound reaches 68% accuracy, with a relatively high number of false-positive results — about 9.2%. Fetal MRI increases the diagnostic accuracy of fetal tumors to 93%, which makes it possible to timely determine the management strategy regarding pregnancy and early postnatal treatment; to increase the chances of a favorable outcome; and to reduce the rates of intrauterine and early neonatal mortality and childhood disability.
Objective: To consider the current capabilities of fetal MRI in neuro-oncology using a broad spectrum of congenital CNS tumors as an example.
Materials and Methods: The paper presents a systematic review of the capabilities of MRI in the differential diagnosis of the most common malignant and benign brain tumors, as well as tumor-like malformations. The issues of the methodology of fetal brain MRI, specific features of MRI semiotics, localization, morphology, postnatal verification, management strategies, and prognosis for various tumor lesions were considered.
Results: Despite the rare occurrence of fetal tumors, the spectrum of diagnostic features for the differential diagnosis of malignant and benign lesions from brain malformations has been well studied. Unlike pediatric neuro-oncology, congenital tumors have а supratentorial localization in 70% of cases. Among malignant tumors, teratomas, gliomas, and choroid plexus tumors are the most common. Among benign tumors, subependymal giant cell astrocytoma, hamartoma, lipoma, and choroid plexus papilloma are the most common. Vascular malformations and thromboses, hematomas, arachnoid cysts, and cerebral manifestations of tuberous sclerosis should be differentiated from tumor lesions. Compliance with the methodology of performing MRI and the use of modern MRI techniques significantly increase the accuracy of prenatal diagnosis of brain tumors.
Conclusions: Fetal MRI provides the necessary information for the differential diagnosis of a broad spectrum of brain tumors and non-neoplastic changes; allows clarification of the morphology and extent of the lesion, determination of the prognosis, and timely prenatal and early postnatal management.

ABSTRACT: Precision oncology seeks to identify patient-specific therapeutic vulnerabilities; however, conventional genomic profiling is limited by intratumoral heterogeneity and its inability to distinguish functional driver alterations from passenger mutations, often resulting in incomplete prediction of therapeutic response. Recently, the combination of CRISPR functional genomics with single-cell multi-omics has proven to be a paradigm-shifting strategy for understanding context-specific cancer vulnerabilities by causal functional interrogation. The aim of this review is to critically examine recent progress in the integration of these technologies for discovering vulnerabilities in cancer, and introduces a new conceptual model, called the Integrated Functional Precision Oncology (IFPO) Framework, that brings together functional genomic perturbation, single-cell multi-omics, computational systems biology, and clinical translation. Literature was retrieved from Pubmed, Web of Science and Google Scholar and peer reviewed studies published between 2020 and 2025. Key findings from historic and recent research were analyzed to pinpoint methodological innovations, translational studies, limitations, and areas in need of further research. The results reveal that the integrated CRISPR–single-cell platforms, such as Perturb-seq, CROP-seq, and ECCITE-seq, can be used to causally interrogate gene function at the single-cell level, allowing for the identification of context-dependent essential genes, synthetic lethal interactions, regulatory networks, and therapeutic resistance mechanisms. All the evidence suggests that therapeutic response is not merely a function of genomic alterations but also the dynamic interplay between genomic alterations, cellular state, epigenetic plasticity, and the tumor microenvironment. The proposed IFPO Framework integrates these findings into a systems-level model that captures the mechanisms by which these functional perturbations, multimodal molecular profiling, and AI-driven integration of data converge to reveal clinically actionable cancer vulnerabilities. This integrated paradigm transforms precision oncology from descriptive molecular profiling to functional systems oncology and offers directions for further progress of precision cancer treatment based on enhanced biomarker discovery, therapeutic target identification, and prospective clinical translation.

ABSTRACT: Prostate cancer still remains one of the most common cancers in men worldwide, and it is a great therapeutic challenge, especially in the field of immunotherapeutics. The tumour microenvironment (TME) is immunologically “cold” in prostate cancer, and influenced by intrinsic molecular characteristics of the disease such as androgen receptor (AR) signalling, PTEN loss, and lineage plasticity towards neuroendocrine prostate cancer (NEPC). Together, these aspects inhibit antigen presentation, block the entry of cytotoxic T cells and help to establish spatially organised immunosuppressive niches, providing a rational explanation for the clinical variability and partial efficacy of immune-based therapies.
Traditional bulk genomic approaches have provided important insights into tumour biology but are unable to capture the cellular and spatial complexity of tumour–immune interactions. These developments have been spurred by recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, which allow to detect individual cell subpopulations within intact tumour tissues, such as exhausted T cells co-expressing PD-1, TIM-3, LAG-3 and TIGIT, immunosuppressive SPP1+ macrophages and various cancer-associated fibroblast subpopulations. These technologies have identified specific immune exclusion sites, stromal–epithelial immune silencing barriers, and therapeutic resistance and immune evasion regulatory programs in the context of prostate cancer specifically.
However, there are still many technical challenges that need to be overcome, such as the lack of patient samples and their demographic diversity, data integration, lack of spatial characterisation of bone metastases and difficulties in clinical translation. Comprehensive multi-omics atlases, AI-driven spatial pattern recognition, functional validation of potential targets and prospective clinical trials based on biomarkers are all important areas for future research. They show significant potential for the creation of better, personalized immunotherapeutic treatment for prostate cancer.

ABSTRACT: Artificial intelligence (AI) has become increasingly integrated into radiology and nuclear medicine, particularly in oncology, where imaging plays a central role in diagnosis, staging, treatment planning, and response assessment. To date, evaluation of AI-enabled radiology has been dominated by diagnostic accuracy metrics derived from retrospective validation studies. While such measures are essential for technical assessment, they provide limited insight into real-world clinical value. High algorithmic performance does not necessarily translate into improved decision-making, workflow efficiency, patient outcomes, or health system performance. This narrative review critically examines AI-enabled radiology as a digital health intervention in oncology and nuclear medicine, emphasizing the need to move beyond accuracy-centric evaluation paradigms. We analyze the translational gap between controlled validation and routine clinical deployment, highlighting challenges related to dataset bias, generalizability, and human–AI interaction. Key domains of real-world impact are explored, including clinical decision-making, multidisciplinary integration, workflow and operational performance, patient-centered outcomes, and system-level implications. Methodological considerations for outcome-focused evaluation are discussed, alongside regulatory, ethical, and governance frameworks necessary for responsible implementation. We propose a clinical-impact–centered evaluation framework that links AI-assisted imaging to patient, clinician, and system-level outcomes within a continuous monitoring model. Reframing AI-enabled radiology as a clinical intervention rather than a standalone algorithm is essential for ensuring meaningful, equitable, and sustainable adoption in oncology and nuclear medicine practice.

ABSTRACT: Liquid biopsies have developed as a revolutionary technique in cancer diagnosis, treatment evaluation, and the detection of therapeutic resistance. Unlike traditional tissue biopsies, which are invasive and limited to a single temporal analysis, liquid biopsies offer a non-invasive, real-time evaluation of tumour dynamics through the analysis of biomarkers such as circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), exosomes, and microRNAs. This approach enables continuous monitoring of tumour advancement, allowing for the early detection of cancer, the tracking of minimal residual disease, and the identification of emerging resistance mutations. As cancers advance and acquire resistance to therapies, liquid biopsy provides critical information that enables clinicians to customise treatment strategies and improve outcomes. Despite challenges such as sensitivity limitations in early-stage cancers and the necessity for standardised testing protocols, technological advancements, including next-generation sequencing (NGS), CRISPR, and AI-driven analytics, are enhancing the precision and accessibility of liquid biopsies. Through ongoing validation and cost-reduction efforts, liquid biopsies are set to become essential to precision oncology, offering a transformative approach to cancer therapy that could improve patient outcomes and foster equitable healthcare globally.

ABSTRACT: This inaugural editorial introduces the new journal, "Oncology, Nuclear Medicine and Transplantology," launched by the National Research Oncology Center (NROC) in Kazakhstan. It outlines the journal's mission to serve as a pivotal interdisciplinary platform integrating these three rapidly evolving and interconnected fields. The editorial emphasizes the journal's commitment to addressing significant healthcare challenges at the national level in Kazakhstan, stimulating regional collaboration across Central Asia, and contributing to the global scientific discourse. The goal is to foster the exchange of original research, clinical experiences, and innovative practices to ultimately improve patient care and advance medical science in these critical specialties.

ABSTRACT: Introduction: This study is aimed at assessing the operational efficiency of the admission department of the National Research Oncological Center (NROC) for the period from 2020 to 2024 with an emphasis on the impact of digitalization on patient management and workflow optimization. Telemedicine is a key tool for improving the availability and quality of medical care, especially for patients living in remote regions. In oncology, its importance is increasing due to the need for interdisciplinary interaction and quick routing of patients.
Methods: A retrospective analysis was conducted using internal hospital records, admission logs, and national healthcare regulations. Key performance indicators were assessed, including patient intake volume, processing time, and rejection rates. The impact of digital tools such as automated registration, routing algorithms, and remote clinical validation was examined.
Results: Patient visits increased from 5,664 in 2020 to 11,851 in 2024, while cancer-related hospitalizations rose from 1,477 to 6,102. The average waiting time for reception was reduced from 12 to 7 hours, and the processing time for documentation was reduced from 45 to 15 minutes. The introduction of digital solutions improved the accuracy of admission and reduced the number of inappropriate hospitalizations. Improvements in identifying clinical contraindications and infectious risks through remote screening technologies were also noted. The number of telemedicine consultations increased 3 times, especially in surgery and transplantology.
Conclusion: Digital transformation has significantly improved admissions efficiency, improving patient flow, reducing processing time and improving decision-making. Further development of digital infrastructure and staff competencies is recommended to ensure sustainable growth and quality of care in cancer care. The comprehensive implementation of telemedicine and interaction with air ambulance contribute to increasing the availability of cancer care, optimizing resources and reducing costs.