03721277

July 22, 2025, 02:40

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Crohn's disease (CD) is a chronic, relapsing inflammatory bowel disease that requires long-term treatment. Infliximab (IFX) plays an important role in inducing remission and maintaining treatment. However, the high incidence of secondary loss of response (SLOR) poses a significant challenge to disease control. Currently, clinical prediction methods for SLOR are limited, and traditional clinical indicators and imaging examinations are not precise enough for prediction. This study, through radiomics technology, integrates the features of the intestinal wall and creeping fat to develop a prediction model that may help identify high-risk patients in advance. This could facilitate earlier adjustment of treatment plans in clinical practice, prevent disease relapse or exacerbation due to SLOR, and reduce unnecessary treatment delays and waste of medical resources. In terms of research methodology, the retrospective analysis used in this study is feasible in practice and makes full use of existing clinical and imaging data. The clinical independent predictors identified through univariate and multivariate analyses, such as white blood cell count, disease duration, and Harvey-Bradshaw Index, are easily obtainable in daily clinical work and are consistent with the factors we clinically observe to be related to SLOR. This makes the study results more easily applicable in clinical practice. The extraction and analysis of radiomics features are also scientifically sound and reasonable. By considering both the intestinal wall and creeping fat, the study fully takes into account the pathological characteristics of Crohn's disease. Creeping fat, as one of the characteristic pathological changes of CD, is increasingly recognized for its role in disease progression and treatment response. Including it in the analysis provides new insights into understanding the pathophysiological mechanisms of CD. The research results are encouraging. The combined prediction model shows good predictive performance in both the training and validation cohorts, indicating high predictive accuracy. This means that the model is highly reliable in practical applications and can provide accurate SLOR risk assessments for clinicians. Through this model, patients can be divided into high-risk and low-risk subgroups to achieve individualized treatment management. For high-risk patients, we can strengthen monitoring, shorten follow-up intervals, and adjust treatment plans in a timely manner, such as increasing IFX dosage or shortening dosing intervals. For low-risk patients, the standard treatment plan can be maintained to avoid overtreatment. This individualized treatment strategy is expected to improve treatment outcomes and patient prognosis. However, the study also has some limitations. The sample size is relatively small, with only 220 patients from two centers included, which may limit the generalizability of the model. In actual clinical applications, patients from different regions and medical centers may vary, so further validation of the model's stability and accuracy is needed in larger-scale multicenter studies. In addition, the study did not include some biomarkers that may have predictive value for SLOR, such as fecal calprotectin. These biomarkers are commonly used in clinical practice to assess intestinal inflammation activity. Including them in the prediction model may further improve its predictive performance. Future studies should consider integrating more clinical indicators and biomarkers to develop a more comprehensive prediction model. Overall, this study provides new ideas and tools for the management of Crohn's disease. As a gastroenterologist, I look forward to further validation and application of this study in clinical practice. Through radiomics technology, we may be able to more accurately predict secondary loss of response to IFX, thereby providing more individualized treatment plans for patients and improving the long-term prognosis of patients with Crohn's disease. At the same time, I also hope that future studies can overcome existing limitations and further optimize the prediction model to provide stronger support for clinical treatment decisions.

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