Muneeb Ul Haq, D. Mark Pritchard, Arthur Sun Myint, Muhammad Ahsan Javed, Carrie A. Duckworth, Ngu Wah Than, Laura J. Bonnett, David M. Hughes
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Using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), each model was evaluated for its risk of bias and applicability. Additionally, the frequency of commonly utilised predictive factors was documented.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Twelve papers discussed fifteen models based on pre-treatment factors. Models predicting response based on the Tumour regression grade (TRG) classified responders as patients who achieved a complete response or near complete response and achieved a pooled AUC of 0.82 (95% CI 0.74–0.89). Models that predicted pathologic complete response (pCR) had a pooled AUC of 0.76 (95% CI 0.71–0.82). The most utilised predictive parameters were age, tumour grade and T stage. However, these models were prone to significant risk of bias and had limited applicability to the general population.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Although the existing models were statistically robust, they lacked broad applicability. This was primarily due to a lack of external validation, which limits their clinical utility. A future CXB-specific model should prioritise dedicated data collection based on pre-calculated sample size and include the predictive factors identified in this review.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"14 7","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.70697","citationCount":"0","resultStr":"{\"title\":\"Clinical Prediction Models for Contact X-Ray Brachytherapy in Managing Rectal Cancers: A Scoping Review\",\"authors\":\"Muneeb Ul Haq, D. Mark Pritchard, Arthur Sun Myint, Muhammad Ahsan Javed, Carrie A. Duckworth, Ngu Wah Than, Laura J. Bonnett, David M. 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引用次数: 0
摘要
背景 目前,还没有临床预测模型可以预测直肠癌对接触式 X 射线近距离放射治疗(CXB)的反应。本综述旨在对试图预测直肠癌对外照射放疗反应的现有模型进行批判性评估,目的是为开发针对 CXB 的预测模型奠定基础。 方法 采用随机效应荟萃分析法计算已发表模型鉴别能力的集合估计值。使用预测模型偏倚风险评估工具(PROBAST)对每个模型的偏倚风险和适用性进行评估。此外,还记录了常用预测因素的频率。 结果 12 篇论文讨论了 15 个基于治疗前因素的模型。根据肿瘤回归分级(TRG)预测反应的模型将反应者归类为获得完全反应或接近完全反应的患者,其集合AUC为0.82(95% CI 0.74-0.89)。预测病理完全应答(pCR)的模型的集合AUC为0.76(95% CI 0.71-0.82)。最常用的预测参数是年龄、肿瘤分级和T分期。然而,这些模型容易出现明显的偏倚风险,对普通人群的适用性有限。 结论 尽管现有模型在统计学上是可靠的,但它们缺乏广泛的适用性。这主要是由于缺乏外部验证,从而限制了其临床实用性。未来的 CXB 专属模型应根据预先计算的样本量优先进行专门的数据收集,并纳入本综述中确定的预测因素。
Clinical Prediction Models for Contact X-Ray Brachytherapy in Managing Rectal Cancers: A Scoping Review
Background
Currently, there are no clinically predictive models that can prognosticate the response of rectal cancers to Contact X-ray brachytherapy (CXB). This review aims to critically evaluate existing models that have attempted to predict the response of rectal cancer to external beam radiotherapy, with the objective of laying the foundation for the development of a CXB-specific prediction model.
Methods
A random-effects meta-analysis was employed to calculate pooled estimates of the discriminative ability of published models. Using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), each model was evaluated for its risk of bias and applicability. Additionally, the frequency of commonly utilised predictive factors was documented.
Results
Twelve papers discussed fifteen models based on pre-treatment factors. Models predicting response based on the Tumour regression grade (TRG) classified responders as patients who achieved a complete response or near complete response and achieved a pooled AUC of 0.82 (95% CI 0.74–0.89). Models that predicted pathologic complete response (pCR) had a pooled AUC of 0.76 (95% CI 0.71–0.82). The most utilised predictive parameters were age, tumour grade and T stage. However, these models were prone to significant risk of bias and had limited applicability to the general population.
Conclusions
Although the existing models were statistically robust, they lacked broad applicability. This was primarily due to a lack of external validation, which limits their clinical utility. A future CXB-specific model should prioritise dedicated data collection based on pre-calculated sample size and include the predictive factors identified in this review.
期刊介绍:
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.