Keyword: Imaging

ABSTRACT: Introduction: Renal cell carcinoma brain metastases are a complication of oncological disease and are associated with an unfavorable prognosis. Stereotactic radiosurgery is one of the main methods of local treatment for intracranial metastases. Data on its outcomes in renal cell carcinoma brain metastases in Kazakhstan are limited.
Objective: To evaluate overall survival, intracranial progression-free survival, and local control after stereotactic radiosurgery.
Materials and Methods: A retrospective cohort study was conducted on patients with renal cell carcinoma brain metastases who underwent stereotactic radiosurgery using the Gamma Knife at the National Center for Neurosurgery between 05.10.2021 and 18.03.2026. The median age was 60 years (44–75), the median Karnofsky Performance Status score was 75 (50–90), and the median prognostic score was 1.5 (0–3.5). Extracranial metastases were present in 25 patients (78.1%), and 21 patients (65.6%) received systemic therapy prior to radiosurgery. Overall survival was assessed in 32 patients; intracranial progression-free survival and local control were assessed in 28 patients with available follow-up magnetic resonance imaging (MRI) studies. For local control assessment, 89 treated lesions were analyzed. Survival rates were calculated using the Kaplan–Meier method. The study was approved by the Bioethics Commission No. 1 of the "National Center for Neurosurgery" JSC (protocol extract No. 7 dated April 13, 2026) and conducted in accordance with the ethical principles of the Declaration of Helsinki.
Results: The median overall survival was 22.1 months. Overall survival rates at 6, 12, and 24 months were 71.4%, 60.8%, and 47.9%, respectively. Among the 28 patients with follow-up MRI studies, the median follow-up period was 4.5 months (1.23–36.13), and the median intracranial progression-free survival was 12.7 months. Intracranial progression was recorded in 11 patients: 9 developed new brain metastases, and 2 had leptomeningeal dissemination. Local control was analyzed in 89 treated metastatic lesions in 28 patients. One case of local progression of a treated lesion was recorded by MRI, detected 9.57 months after treatment. Local control at 6 and 12 months was 100% and 96.8%, respectively.
Conclusions:  Stereotactic radiosurgery using the Gamma Knife provides high local control of renal cell carcinoma brain metastases. Intracranial progression was predominantly due to the appearance of new metastatic lesions and leptomeningeal dissemination, rather than progression of previously treated lesions. The obtained results demonstrate the high efficacy of the Gamma Knife in achieving local control in patients with renal cell carcinoma brain metastases.

ABSTRACT: Background: Dense breast tissue reduces mammographic sensitivity and raises breast cancer risk. Supplemental imaging and artificial intelligence supported mammography reading have been tested prospectively. The European Society of Breast Imaging recommends supplemental magnetic resonance imaging for extremely dense breasts, whereas the American College of Physicians in 2026 advised against it for average risk women with dense breasts. This review summarises what the principal studies measured and found.
Objective: To review the published evidence on both strategies in dense breasts, reporting each study's endpoints and results.
Materials and Methods: Narrative review of primary publications, published online 2019 to 2025, quoted verbatim. Four large prospective multicentre studies reporting on dense breasts were selected, two per strategy: the randomised trials DENSE (Netherlands, magnetic resonance imaging) and BRAID (United Kingdom, abbreviated magnetic resonance imaging, automated ultrasound and contrast-enhanced mammography), and the observational studies PRAIM (Germany, artificial intelligence supported double reading) and ASSURE (United States, artificial intelligence supported single reading with safeguard review).
Results: DENSE randomised 40,373 women with extremely dense breasts and normal mammography to invitation for supplemental magnetic resonance imaging or to mammography alone: interval cancers 2.5 versus 5.0 per 1000; among the 59% accepting, detection was 16.5 and false positives 79.8 per 1000. BRAID randomised 9361 women with dense breasts and a negative mammogram: detection was 17.4 per 1000 examinations with abbreviated magnetic resonance imaging, 19.2 with contrast-enhanced mammography and 4.2 with automated ultrasound, the contrast-based modalities not differing significantly; recall 9.7%, 9.7% and 4.0%, median invasive size 10, 11 and 22 millimetres. In PRAIM (463,094 women), artificial intelligence supported double reading was associated with detection of 6.7 versus 5.7 per 1000 (17.6% higher) and non-inferior recall; in dense breasts the 18.7% increase was not statistically significant. In ASSURE (579,583 tomosynthesis examinations, single reading), detection with the artificial intelligence workflow was 5.6 versus 4.6 per 1000 (21.6% higher), recall 11.1% versus 10.6%, and detection in dense breasts 22.7% higher.
Conclusions: In BRAID, abbreviated magnetic resonance imaging and contrast-enhanced mammography detected three times as many invasive cancers as automated ultrasound, at half the size with more than twice the recall, and did not differ significantly from each other. In DENSE, invitation to supplemental magnetic resonance imaging halved the interval cancer rate. Artificial intelligence support was associated with higher detection in PRAIM without higher recall and in ASSURE with slightly higher recall; neither was randomised. No included study measured breast cancer mortality; survival benefit remains undemonstrated and overdiagnosis unquantified.

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: Spatial tumour heterogeneity, which denotes the changes in cellular and molecular attributes across distinct locations within a tumour, significantly influences cancer diagnosis and treatment resistance. The heterogeneity of tumour cells inside a singular mass facilitates tumour development, metastasis, and the ineffectiveness of standard therapy. Comprehending the geographical distribution of tumour cells is crucial for formulating more efficient treatment regimens. Diverse methodologies are employed to investigate spatial heterogeneity, encompassing modern imaging techniques such as MRI, PET, and multiplexed imaging, alongside omics approaches including genomes, transcriptomics, and proteomics. These instruments offer insights into the tumour microenvironment and facilitate the identification of resistant subpopulations. The amalgamation of imaging and genomic data via radiogenomics has emerged as a viable methodology, providing an extensive perspective on the spatial and molecular intricacies of tumours. Principal findings reveal that spatial heterogeneity fosters medication resistance by establishing microenvironments characterised by varying oxygen levels, immunological infiltration, and genetic alterations, hence complicating the efficacy of monotherapy strategies. Hypoxic environments and immunological evasion significantly contribute to treatment resistance. Addressing geographical heterogeneity has the potential to enhance cancer treatments. By analysing the molecular and geographical characteristics of tumours, physicians can customise therapies more efficiently, minimising resistance and improving therapeutic results. This methodology signifies a vital advancement in precision medicine, providing more individualised and efficacious cancer therapies in the future.

ABSTRACT: Summary: The article presents the results of evaluating the diagnostic efficiency of ultrasound (US) in determining the extent of pathological processes in lymphoma patients. The study included 48 patients with Hodgkin’s and non-Hodgkin’s lymphoma who were hospitalized at the National Scientific Oncology Center from 2021 to 2024. All participants underwent comprehensive ultrasound of the abdominal parenchymal organs and lymph nodes using B-mode and Doppler imaging. The obtained data indicate high sensitivity and specificity of US in detecting liver, spleen, and abdominal lymph node involvement. Characteristic echographic signs of lymphatic conglomerates and associated complications such as ascites and exudative pleuritis were noted. Given the availability and safety of the method, US can be considered a key tool for primary diagnosis and lymphoma monitoring in clinical practice.
The purpose of the study: To evaluate the diagnostic efficiency of ultrasound (US) in assessing the extent of the disease in lymphoma.
Methods: A retrospective analysis of 48 medical records of patients with confirmed diagnoses of Hodgkin’s lymphoma (HL) and non-Hodgkin’s malignant lymphoma (NHL) who were treated at the National Scientific Oncology Center between 2021 and 2024. All patients underwent comprehensive abdominal ultrasound, pleural ultrasound, and lymph node imaging with Doppler.
Results: Pathological changes in lymph nodes, spleen, liver, and pleura were detected in all patients. In 60% of cases, the lymph node conglomerates appeared echonegative, while 5% showed signs of aggressive progression (undefined capsules, liquefaction). Typical echographic signs of diffuse and focal organ changes were observed.