Technology in Cancer Research & Treatment最新文献

{"title":"Dosimetric and Radiobiological Impact of Patient Setup Errors in Intensity-modulated Radiotherapy for Esophageal Cancer.","authors":"Jia-Huan Cai, Xun Peng, Jia-Yang Lu","doi":"10.1177/15330338241311136","DOIUrl":"10.1177/15330338241311136","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the impact of patient setup errors on the dosimetry and radiobiological models of intensity-modulated radiotherapy (IMRT) for esophageal cancer.</p><p><strong>Methods and materials: </strong>This retrospective study with 56 patients in thermoplastic mask (TM) and vacuum bag (VB) groups utilized real setup-error (RSE) data from cone-beam CT scans to generate simulated setup-error (SSE) data following a normal distribution. The SSE data were applied to simulate all treatment fractions per patient by shifting the plan isocenter and recalculating the dose. A simulated plan sum (SPS) was created by accumulating all simulated fraction plans. Comparisons of target dose, improved homogeneity index (iHI), conformity index (CI), tumor control probability (TCP) and normal tissue complication probability (NTCP) were conducted between SPSs and original treatment plans (OTPs). Correlations between RSE and TCP/NTCP were analyzed.</p><p><strong>Results: </strong>Compared to OTPs in the TM group, the planning target volume (PTV) of SPSs showed reductions in D_95%, D_98%, iHI, CI and TCP by 1.2%, 2.2%, 2.3%, 7.3% and 1.2%, while D_2% increased by 0.3%; D_2% of clinical target volume (CTV) increased by 0.2% (<i>P </i>< .05). In the VB group, D_95%, D_98%, iHI, CI and TCP of PTV decreased by 2.5%, 4.5%, 4.2%, 15.6% and 2.0%, with D_2% increasing by 0.5%; D_2% of CTV increased by 0.5% while D_98% decreased by 0.2% (<i>P </i>< .05). The dose of organs at risk (OARs) changed slightly in both groups. The mean and standard deviation of absolute RSE negatively correlated with the TCP of PTV, while the mean RSE positively correlated with the NTCP of lung and spinal cord.</p><p><strong>Conclusions: </strong>Setup errors may reduce dose homogeneity and conformity, potentially reducing TCP of PTV and increasing NTCP, especially when mean RSE shifts the isocenter towards OARs. VB immobilization may result in relatively larger impacts of setup errors, but this needs future validation.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338241311136"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.","authors":"Boya Liu, Xiangrong Gu, Danling Xie, Bing Zhao, Dong Han, Yuli Zhang, Tao Li, Jingqin Fang","doi":"10.1177/15330338251334453","DOIUrl":"https://doi.org/10.1177/15330338251334453","url":null,"abstract":"<p><p>IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and evaluate machine learning models using ultrasound-derived radiomics and clinical features to predict TIL levels in BC.MethodsThis retrospective study included 256 BC patients between January 2019 and August 2023, who were randomly divided into training (n = 179) and test (n = 77) cohorts. Radiomics features were extracted from the intratumor and peritumor regions in ultrasound images. Feature selection was performed using the \"Boruta\" package in R to iteratively remove non-significant features. Extra Trees Classifier was used to construct radiomics and clinical models. A combined radiomics-clinical (R-C) model was also developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical utility. A nomogram was created based on the best-performing model.ResultsA total of 1712 radiomics features were extracted from the intratumor and peritumor regions. The Boruta method selected five key features (four from the peritumor and one from the intratumor) for model construction. Clinical features, including immunohistochemistry, tumor size, shape, and echo characteristics, showed significant differences between high (≥10%) and low (<10%) TIL groups. Both the R-C and radiomics models outperformed the clinical model in the test cohort (area under the curve values of 0.869/0.838 vs 0.627, <i>P</i> < .05). Calibration curves and Brier scores demonstrated superior accuracy and calibration for the R-C and radiomics models. DCA revealed the highest net benefit of the R-C model at intermediate threshold probabilities.ConclusionUltrasound-derived radiomics effectively predicts TIL levels in BC, providing valuable insights for personalized treatment and surveillance strategies.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251334453"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrospective Insights into the Clinicopathological Features and Treatment Outcomes of Thoracic SMARCA4-Deficient Tumors.","authors":"Lijin Chen, Chunyang Su, Jiadi Yao, Xiaofeng Li, Xiaoyan Lin","doi":"10.1177/15330338251345377","DOIUrl":"10.1177/15330338251345377","url":null,"abstract":"<p><p>IntroductionThoracic SMARCA4-deficient tumors, which are rare and aggressive malignancies found in the lung or thoracic cavity, present a challenge in treatment standardization. This challenge arises from their resistance to chemotherapy and the absence of targeted therapy options.MethodsThoracic SMARCA4-deficient tumors were identified retrospectively using pathology databases. The clinicopathological characteristics of these tumors are outlined, and the clinical outcomes of advanced patients treated with immune checkpoint inhibitors (ICIs) in combination with chemotherapy and chemotherapy alone are reviewed.ResultsThirty-nine patients had thoracic SMARCA4-deficient tumors, with a median age of 62 years. The cohort consisted of 92.3% males, and 89.7% had a history of smoking. Within this group, 94.9% had stage III/IV disease at diagnosis. SMARCA4-deficient non-small cell lung cancer (SMARCA4-DNSCLC) and SMARCA4-deficient undifferentiated tumors (SMARCA4-DUT) display distinct histological and immunohistochemical features. Thirty-five patients underwent systemic therapy, achieving an ORR of 51.4%, a DCR of 82.9%, and a median OS of 20.9 months. Patients were categorized into chemotherapy (28.6%) and ICIs plus chemotherapy (71.4%) groups. The ICIs plus chemotherapy group exhibited an ORR of 64.0% and a DCR of 96.0%, while the chemotherapy group had an ORR of 20.0% and 50.0%, respectively (<i>P</i> < .0001 for ORR and DCR). The median OS for ICIs plus chemotherapy and chemotherapy groups were 20.9 months and 6.5 months, and median PFS were 9.6 months and 3.5 months, respectively, all statistically significant (<i>P</i> < .05). Multivariate COX regression analysis indicated that treatment was an independent prognostic factor for OS.ConclusionThoracic SMARCA4-deficient tumors exhibit a lack of SMARCA4 expression, displaying high malignancy and aggressiveness while exhibiting poor response to standard chemotherapy. The combination of ICIs with chemotherapy could potentially serve as an effective treatment approach for thoracic SMARCA4-deficient tumors.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251345377"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment.","authors":"Yihao Zhao, Cuiyun Yuan, Ying Liang, Yang Li, Chunxia Li, Man Zhao, Jun Hu, Ningze Zhong, Wei Liu, Chenbin Liu","doi":"10.1177/15330338251345895","DOIUrl":"10.1177/15330338251345895","url":null,"abstract":"<p><p>PurposeAutomating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. Manual QA is tedious, time-consuming, and prone to subjective experiences. Automatic segmentation reduces physician workload and improves consistency. However, an effective QA process for these automatic contours remains an unmet need in clinical practice.Materials and MethodsThe patient data used in this study was derived from the AAPM Thoracic Auto-Segmentation Challenge dataset, including left and right lungs, heart, esophagus, and spinal cord. Two groups of organ-at-risk (OAR) were generated. A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as 'high' or 'low' quality. To evaluate the generalizability, we generated low-quality contours using translation and resizing techniques and assessed correlations between detection limits and metrics such as volume, Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD).ResultsThe proposed OC-SVM model outperformed binary classifiers n metrics such as balanced accuracy and area under the receiver operating characteristic curve (AUC) . It demonstrated superior performance in detecting various types of contour errors while maintaining high interpretability. Strong correlations were observed between detection limits and contour metrics.ConclusionOur proposed model integrates an attention mechanism with a one-class classification framework to automate QA for OAR delineations. This approach effectively detects diverse types of contour errors with high accuracy, significantly reducing the burden on physicians during radiotherapy planning.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251345895"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of Radiotherapy Fiducial Marker Tracking via Single-Shot X-ray Acoustic Tomography.","authors":"Norman Alexis Cantú-Delgado, Héctor Mauricio Garnica-Garza","doi":"10.1177/15330338251342867","DOIUrl":"10.1177/15330338251342867","url":null,"abstract":"<p><p>Introductionin radiotherapy, fiducial markers improve the accuracy of radiation delivery, and their use has become increasingly important in the treatment of various cancers, particularly those in the prostate and lung. This work aims to determine, via Monte Carlo simulations and numerical ultrasound transport, the feasibility of fiducial marker localization via single-shot x-ray acoustic computed tomography.Methodspatient data from CT scans for two treatment sites, prostate and lung, were used to model the fiducial marker localization process. Monte Carlo simulation was used to calculate the absorbed dose distribution in each patient resulting from the irradiation with a 120 kVp x-ray imaging source, assuming that the dose is imparted in a short pulse. Ultrasound transport through each patient was modeled with the numerical ultrasound transport package k-Wave. For the image reconstruction process, as the exact internal patient structure will not be known at the time of treatment, a homogenous medium with the patient external contour and dimensions was used.ResultsIt is shown that the use of a homogeneous model to approximate the actual patient material composition during the reconstruction process, necessary as the geometry of the internal structures is not known at the time of the treatment, severely degrades the quality of the x-ray acoustic tomography images, but that it is still possible to determine the fiducial marker position with an accuracy of or better than 1 mm. The largest errors are observed for the lung patient when the lung is in an inflated state.Conclusionsit has been shown that single-shot x-ray acoustic tomography can be an effective tool for the tracking and localization of radiotherapy fiducial markers, exhibiting an accuracy of better than 1 mm, despite the poor visual quality of the resultant images.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251342867"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12227932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144554937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of the Residual Errors on the Dose for Left-Sided Breast Cancer Radiotherapy After Translation Error Correction Based on CBCT.","authors":"Ya Wang, Lu Zeng, Pan Gong, Denghong Liu, Qianqian Meng, Konglong Shen, Zhihui Liu, Renming Zhong","doi":"10.1177/15330338251338489","DOIUrl":"10.1177/15330338251338489","url":null,"abstract":"<p><p>ObjectiveThis study analyzed the dosimetric impact of residual errors (rotational and deformation errors) in left-sided breast cancer radiotherapy after cone-beam CT (CBCT)-based translational errors correction.MethodsTwenty patients treated with intensity-modulated radiotherapy (IMRT) were retrospectively analyzed. Virtual CT images were generated by deforming and registering CBCT images with planning CT images. The accumulated dose was calculated to assess residual errors effects on target and organs at risk (OARs). A phantom test was conducted to evaluate rotational errors impacts.ResultsResults showed significant dose differences: for 4005 cGy, D98 and D95 of the breast (PTV_b) decreased, and mean dose, V30, and V20 of the left lung reduced; for 5000 cGy, D98 of the supraclavicular lymph nodes (PTV_sc) and PTVb, D95 of PTV_b, and mean dose and V20 of the heart differed significantly. Phantom simulations revealed that pitch angles ≤-1.8° and roll/yaw angles >2° caused overdosing in the left lung and heart, with maximum dose differences of 31.89% (heart) and 19.19% (lung) for 4005 cGy, and 26.32% (heart) and 20.92% (PTV_sc) for 5000 cGy.ConclusionResidual errors significantly affect dose distribution despite CBCT-based translational correction. Improved immobilization techniques or 6DOF couch correction are recommended to mitigate rotational errors.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251338489"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-Efficient Early Diagnostic Tool for Lung Cancer: Explainable AI in Clinical Systems.","authors":"Anu Maria Sebastian, David Peter, T P Rajagopal, Rinu Ann Sebastian","doi":"10.1177/15330338251370239","DOIUrl":"10.1177/15330338251370239","url":null,"abstract":"<p><p>IntroductionLung cancer has the highest mortality rate among all cancer types globally, largely due to delayed or ineffective diagnosis and treatment. Radiomics is commonly used to diagnose lung cancer, especially in later stages or during routine screenings. However, frequent radiological imaging poses health risks, and while advanced diagnostic alternatives exist, they are often costly and accessible only to a limited, privileged population. Leveraging clinical data using machine learning (ML) and artificial intelligence (AI) enables a safer, more inclusive, and affordable solution. Due to a lack of interpretability, AI-based models for cancer diagnosis are less adopted by clinicians.MethodsThis study introduces a safe, inclusive, and cost-effective lung cancer diagnostic method using an explainable AI (XAI) model built on routine clinical data. It employs a stacking ensemble of Artificial Neural Network (ANN) and Deep Neural Network (DNN) to match the diagnostic performance of clean-data DNN models. By incorporating rare medical cases through Adaptive Synthetic Sampling (ADASYN), the model reduces the risk of missing challenging, rare-case diagnoses.ResultsThe proposed XAI model demonstrates strong performance with an accuracy of 0.8558, AUC of 0.8600, precision of 0.8092, recall of 0.9282, and F1-score of 0.8646, notably improving rare case detection by over 50%. SHapley additive exPlanations(SHAP)-based interpretability highlights Erythrocyte sedimentation rate(ESR), intoxication-related factors, hemoglobin levels, and neutrophil counts as key features. The model also reveals associations, such as a link between heavy tobacco use and elevated ESR. Counterfactual explanations help identify features contributing to misdiagnoses by exposing sources of confusion in the model's decisions.ConclusionGiven the limited dataset size and geographic constraints, this research should be viewed as a prototype and in its current form, the model is best suited as a pre-screening tool to support early detection. With training on larger and more diverse datasets, the model has strong potential to evolve into a robust and scalable diagnostic solution.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251370239"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12357035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic Biomarkers for Papillary Thyroid Cancer: Reducing Overtreatment, Improving Clinical Efficiency, and Enhancing Patient Experience.","authors":"Oliver F Bathe, Cynthia Stretch","doi":"10.1177/15330338251361633","DOIUrl":"10.1177/15330338251361633","url":null,"abstract":"<p><p>Papillary thyroid cancer (PTC), the most prevalent form of thyroid malignancy, is generally indolent but poses a recurrence risk of 10%-15%, leading to a clinical paradox: the need to mitigate recurrence while avoiding overtreatment. Current prognostic frameworks, reliant on anatomical and histopathological factors, often result in inefficient treatment pathways, unnecessary surgical interventions, and increased patient burden. The advent of molecular diagnostics presents a paradigm shift in risk stratification. Implementing preoperative molecular tests could transform PTC management by enabling tailored therapeutic strategies, reducing the need for completion thyroidectomies, optimizing the selection of patients for active surveillance, and refining the use of adjuvant therapies such as radioactive iodine. While genomic alterations such as <i>BRAF</i> and <i>TERT</i> mutations have been explored as prognostic markers, their predictive utility remains limited. In contrast, transcriptomic profiling has emerged as a powerful tool for identifying aggressive PTC subtypes with greater precision. Transcriptomic-based prognostic tests, like the novel Thyroid GuidePx^® classifier, effectively stratify PTCs into distinct molecular subgroups with differing recurrence risks, surpassing traditional clinicopathological models in predictive accuracy. By shifting toward biologically informed decision-making, we can enhance clinical efficiency, minimize patient morbidity, and improve overall healthcare resource utilization.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251361633"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinicopathological Characteristics and Prediction of Postoperative Mortality Risk in Patients with Non-metastatic Sarcomatoid Renal Cell Carcinoma.","authors":"Lian Fang, Zhiyu Zhang, Ouyang Song, Yufeng Hou, Hujuan Yang, Jun Ouyang, Xuefeng Zhang, Nan Wang, Shicheng Sun","doi":"10.1177/15330338251367123","DOIUrl":"https://doi.org/10.1177/15330338251367123","url":null,"abstract":"<p><p>IntroductionSarcomatoid renal cell carcinoma (sRCC) is rare but highly aggressive and is associated with poor prognosis and limited treatment responsiveness. Despite several studies investigating its clinicopathological features, existing research is often limited by small sample sizes and short follow-up periods, and currently, no prognostic risk model is specific to patients with non-metastatic sRCC. This study aimed to investigate the clinicopathological characteristics of patients with non-metastatic sRCC and develop a predictive model for postoperative mortality risk.MethodsIn this retrospective study, we analyzed the clinical data of 45 patients diagnosed with non-metastatic sRCC who underwent surgical treatment at our institution's Department of Urology, between January 2008 and June 2024. These patients were compared with 527 patients with non-sarcomatoid renal cell carcinoma (non-sRCC). The primary endpoint was death, and the exact cause of death was recorded. Routine postoperative examinations and treatment details were documented through outpatient and inpatient electronic medical record systems.ResultsThe results indicated significant differences in body mass index, hypertension, surgical approach, nephrectomy type, surgical duration, maximum tumor diameter, tumor necrosis, T stage, and Ki-67 expression between patients with sRCC and those with non-sRCC (<i>P</i> < 0.05). Survival analysis revealed that the cancer-specific survival (CSS) for patients with sRCC was significantly lower than that for patients with non-sRCC (<i>P</i> < 0.001). Cox univariate and multivariate analyses identified maximum pathological tumor diameter, T stage, and high Ki-67 expression as independent risk factors. Based on these factors, we developed a postoperative mortality risk prediction model for patients with sRCC, with the calibration curves demonstrating a good fit of the model.ConclusionsThe proposed model is designed for patients with non-metastatic sRCC. It has potential clinical application value, aiding in the identification of high-risk patients and providing guidance for individualized treatment and close follow-up.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251367123"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Bharat Cancer Genome Atlas: Charting India's Unique Cancer Landscape for Precision Oncology.","authors":"Sundarasamy Mahalingam, Vinod Scaria, Sridhar Sivasubbu","doi":"10.1177/15330338251381404","DOIUrl":"10.1177/15330338251381404","url":null,"abstract":"<p><p>Development of the Bharat Cancer Genome Atlas (BCGA) is poised to be a comprehensive genomic database which will not only deepen our scientific understanding of the unique molecular landscape of cancers prevalent in India but also provide the essential foundation required to facilitate the development of targeted therapies, enable personalized treatment strategies, and foster the creation of more effective early detection methods specifically tailored for the Indian population. The open-access nature of the BCGA is a core strength, designed to democratize access to this vital information, thereby empowering researchers to make new discoveries, enabling clinicians to provide more precise care, and allowing patients and their families to engage more fully in their health journey.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251381404"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}