Colorectal cancer is currently the third most common malignant tumor worldwide and the second leading cause of cancer-related death globally (1). The high mortality rate among colorectal cancer survivors can be attributed to disease recurrence, with an estimated 29%–63% of patients diagnosed with locally advanced disease experiencing relapse. The TNM staging system, based on tumor infiltration depth (T), lymph node metastasis (N), and distant metastasis (M), is closely associated with the five-year survival rate of colorectal cancer patients (2). However, apart from TNM staging, there are few robust predictive indicators for long-term survival.

Although screening methods and treatment strategies have advanced in recent years, research on cancer prevention and therapeutic biomarkers has never ceased. Well-known mutations in colorectal cancer cells include KRAS, TP53, and others (3, 4). While these studies provide some guidance for colorectal cancer diagnosis and intervention (5), there remains a lack of convincing quantitative markers for prognostic prediction, aside from the TNM staging system.

The growth of colorectal cancer is accompanied by the activation of numerous biological processes, such as angiogenesis, altered cellular metabolism, and increased glucose consumption. These processes contribute to tumor heterogeneity, characterized by abnormal, irregular, and disordered tissue structures, along with high cellular density, hypoxia, necrosis, hemorrhage, and mucinous changes. Tumor heterogeneity tends to evolve over time, increasing with tumor progression and affecting local and distant invasion, chemotherapy delivery, and cellular resistance to chemotherapy and radiotherapy, ultimately impacting prognosis (6). Given this complex disease progression, our goal is to identify subtle clues amidst this chaos, enabling early diagnosis and intervention to improve patient outcomes.

For patients with locally advanced colorectal cancer, accurately predicting long-term survival during the perioperative period could provide crucial insights for individualized treatment planning, including neoadjuvant and adjuvant chemoradiotherapy. This approach could significantly reduce recurrence rates and mortality (7). Currently, advanced imaging technologies such as CT, MRI, and PET-CT offer opportunities to obtain structural, functional, and molecular information about tumors (8). Among these, CT remains the most time-efficient and cost-effective modality. However, CT image interpretation primarily relies on human expertise, which can be prone to errors, particularly in cases where the radiologist lacks experience. Even experienced radiologists may disagree on complex cases, primarily due to the inherent subjectivity in CT image interpretation. Moreover, traditional quantitative indicators from CT, such as Hounsfield units, have limited predictive power regarding patient prognosis, and research into prognostic prediction remains sparse.

In recent years, with the rapid advancement of artificial intelligence (AI), there have been profound changes in various sectors, including healthcare. AI has also found applications in medical imaging, particularly in texture analysis (TA), a radiomics technique that quantifies medical images. TA enables a detailed assessment of the heterogeneity of lesions and surrounding tissues, addressing the limitations of human vision, and offering precise, quantitative data extraction from medical images. TA has been widely applied in the preoperative diagnosis, pathological grading, and prognostic prediction of pancreatic cancer (9, 10). Texture analysis is based on traditional medical imaging, and it quantifies tumor heterogeneity by analyzing the distribution and relationships of pixel or voxel grayscale values in the lesion and surrounding tissues (11, 12). As a non-invasive imaging tool, TA has shown great potential in the diagnosis, treatment assessment, and prognosis of rectal cancer (13).

In this study, our team collected texture analysis data from colorectal cancer patients who underwent radical surgery at our center in 2019. By integrating this data with general clinical information and TNM staging, as well as the five-year survival data of the patients, we aim to develop a prognostic model for estimating the survival time of colorectal cancer patients. Utilizing machine learning-based dimensionality reduction techniques and visual tools such as nomograms, our goal is to provide a more robust foundation for personalized diagnosis and treatment. Our article is compliant with the TITAN Guidelines 2025 (14).

Firstly, the MaZda software was used to delineate the regions of interest (ROIs) from the CT images of 236 patients, from which over 300 texture analysis quantitative features, including Teta1, Teta4, WavEnLL_s-2 and GrSkewness were extracted. These 302 variables, combined with patient data such as age, sex, alcohol consumption, CEA, CA199 levels, and TNM staging, were analyzed in correlation with patient survival data. The survival status at 1, 3, and 5 years was used as the outcome variable, with survival or death as the gold standard. LASSO regression and nomograms were employed to perform dimensionality reduction and visualize predictive models for 1-year, 3-year, and 5-year survival, and we performed 10-fold cross-validation to determine the optimal lambda value (the regularization parameter in LASSO regression), which was used to fit the final LASSO model. The cross-validation curves of LASSO regression were plotted (Figures 3a, c, and Supplementary Figure S1a). Additionally, coefficient path plots were generated with the optimal lambda value indicated as a reference line (Figure 2a). For the 1-year data, the optimal lambda value was 0.0296. The variables selected included CA199, CEA, stage, Horzl_RLNonUni, GrSkewness, and WavEnLL_s-5. After multivariate stepwise regression, the corresponding nomogram was generated (Supplementary Figure S1b). Similarly, for the 3-year and 5-year data, the optimal lambda values were 0.0256 and 0.0802, respectively, and after stepwise regression, their respective nomogram prediction models were generated (Figures 3b, d).

It is evident from the analysis that, in addition to common preoperative data such as tumor markers (e.g., CA199, CEA), and general patient information (e.g., age, sex), as well as TNM staging data, the CT texture analysis variables showing significant predictive value include Teta1, Teta4, WavEnLL_s-2, GrSkewness, Horzl_RLNonUni, and WavEnLL_s-5. Among these, Teta1, Teta4, and WavEnLL_s-2 demonstrated strong prognostic significance in the 3-year and 5-year prediction models (Table 1), while Horzl_RLNonUni, GrSkewness, and WavEnLL_s-5 showed notable predictive value for the 1-year model (Table 1). Furthermore, the forest plot analysis revealed statistically significant results for Teta1, Teta4, and WavEnLL_s-2 (Supplementary Figure S2).

Additionally, we found that TNM staging inherently holds predictive advantages for prognosis (Table 1). The results clearly indicate that patients with advanced-stage colorectal cancer have a significantly shorter expected survival time compared to those with non-advanced disease (Figure 4). However, its ability to predict specific survival times remains limited. In the ROC curve analysis, we observed that the AUC for CT texture analysis variables was 0.801, while the AUC for TNM staging data was 0.751. When CT texture analysis and TNM staging were combined, the overall AUC increased to 0.807 (Figure 5). Finally, we incorporated both radiomics features and TNM staging alone as variables in the predictive models. To quantitatively compare the performance improvement between models with different variable combinations, our research team employed Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) for further evaluation. We calculated net reclassification improvement (NRI=0.460, p<0.01) and integrated discrimination improvement (IDI=0.217, p<0.05), demonstrating significant reclassification improvement. This indicates that, in combination with traditional TNM staging, CT texture analysis significantly supplements aspects not covered by TNM staging, offering a more comprehensive prediction model for patient prognosis. Furthermore, we performed a survival curve analysis on the 236 enrolled colorectal cancer patients to visually evaluate their five-year survival outcomes.

To further evaluate the predictive performance of the LASSO-derived radiomics model, we constructed decision curves and calibration curves. As shown in Figure 2b, the LASSO model demonstrated significant clinical utility, particularly at intermediate-to-low risk thresholds, where its decision-making efficacy was most pronounced. Figure 2c illustrates the calibration curve after 500 bootstrap iterations, revealing near-perfect alignment between predicted and observed mortality probabilities, indicating excellent calibration and high reliability. Furthermore, external validation with an independent cohort of 200 cases yielded an AUC of 0.814 (Figure 2d), which showed no significant difference compared to the original training cohort AUC of 0.807, confirming the robust predictive performance of our model.

The purpose of this study was to incorporate key imaging information, which is considered crucial in clinical practice, into the research framework and develop a novel predictive approach. By utilizing real-world data from a five-year follow-up at our center, we aimed to construct a prediction model. As described earlier, using MaZda software, 302 quantitative texture features of tumor tissue were derived and combined with patient demographics, along with survival data over five years. Univariate and multivariate Cox regression analyses, LASSO regression, nomogram analysis, forest plots, and ROC curve analysis were performed. Through dimensionality reduction with the machine learning techniques mentioned above and deep integration with traditional TNM staging data, we were able to more accurately predict the approximate survival status of colorectal cancer (CRC) patients over the next five years during the perioperative period, providing strong support for clinical decision-making.

Reviewing traditional imaging diagnostic experience, experienced experts typically rely on the degree of tumor enhancement and the presence of significantly enlarged lymph nodes around the tumor to guide diagnosis. More specifically, lymph node metastasis is tracked to different stations. Traditional quantification tools, such as CT values, are commonly used. Tumor markers such as CEA and CA199, which are recognized to have certain guiding significance for tumor progression, also serve as useful references for assessing the severity of tumors. However, traditional analysis methods often fail to provide homogeneous conclusions about tumor malignancy, progression, and expected survival time, heavily depending on the clinical experience of the physician. With the addition of traditional TNM staging information, clinicians can make a general prognosis based on serum markers and basic patient information, such as age, sex, smoking, drinking habits, and BMI. Yet, preoperative CT imaging, which is crucial for assessing tumor prognosis, lacks further quantifiable data to support such predictions, which is a significant limitation.

In recent years, with the rapid development of artificial intelligence (AI), numerous imaging radiomics studies have been conducted on CT imaging in CRC. We are now shifting focus not only on qualitative analysis of images but also on accurate quantitative diagnostic work. Texture analysis (TA) has been analyzed for its potential and feasibility in assisting precise diagnosis (8). Recent literature has increasingly highlighted the utility of CT as a non-invasive diagnostic tool. It has been found that CT can distinguish between benign and malignant colorectal tumors in asymptomatic patients (17, 18). Volumetric analysis suggests that for tumors larger than 3 cm, texture analysis can nearly match human experience in distinguishing benign from malignant tumors, although it remains complementary. Studies have shown that TA significantly outperforms resident physicians in classifying CRC, inflammatory bowel disease (IBD), and normal thickening of the colon (NTC), though there is no significant difference when compared with experienced radiologists (19). In the early stages of this study, we also performed a series of validation tests on this result, confirming that CT radiomics data can assist in preoperative assessment of tumor benignancy and approximate malignancy (Table 2). Furthermore, image-based radiomics using pre-processed CT data has been shown to predict the outcomes of hepatic arterial infusion chemotherapy (HAIC) for advanced inoperable CRC with high accuracy and feasibility (13). Texture analysis data based on MRI and PET-CT can assist in predicting CRC recurrence risks and evaluating the effects of radiotherapy (20, 21).

However, there are few reports on studies analyzing the correlation between CT-based radiomics features and patient survival prognosis, as well as the construction of predictive models. Instead, most research has focused on utilizing MRI and PET-CT imaging data, which exhibit relative disadvantages in terms of clinical generalizability and convenience compared to CT examinations. Abdominal CT is relatively low-cost and enjoys higher public acceptance. Moreover, during the initial diagnostic process, clinicians are more inclined to prescribe abdominal CT examinations. Additionally, for the establishment of prognostic models, this study investigates the correlation between radiomics features and patient survival time, thereby constructing 1-year, 3-year, and 5-year predicted prognostic model. Compared to the traditional TNM staging system, radiomics features demonstrate superior predictive capability for prognosis. This approach allows for a preoperative estimation of patient survival time, providing an additional dimension of evidence to support personalized clinical decision-making regarding treatment strategies.

For patients with CRC liver metastases, TA can predict the efficacy of different chemotherapy regimens (22). For CRC patients without distant metastases, image-based global tumor features have been shown to predict pathological staging of rectal cancer preoperatively, thus providing a theoretical basis for individualized treatment, adjuvant chemotherapy, and even neoadjuvant chemotherapy (23).

Texture analysis also demonstrates diagnostic advantages for diseases in other systems. In the context of the global COVID-19 pandemic, current diagnostic guidelines recommend RT-PCR testing. As an auxiliary diagnostic tool, chest CT has been shown to reveal visual features of COVID-19 and provides clear guidance for several stages of the disease. A semi-supervised learning framework for 3D segmentation of COVID-19 infection areas has been proposed from chest CT scans to achieve accurate diagnosis and treatment (24). Texture analysis can also play a guiding role in clinical staging prediction and recurrence risk assessment of lung cancer, as well as in diagnosing and differentiating pancreatic neuroendocrine tumors based on enhanced CT radiomics features (25–27). Additionally, for diseases that are difficult to biopsy preoperatively, texture analysis has proven to be a strong aid in the prognosis and diagnosis of glioblastoma and brain metastases (28, 29).

In recent years, especially in the last two years, the rapid advancements in artificial intelligence and computer technology have shown clear advantages over humans in many fields (30). This study, by utilizing a visualized prognosis prediction model, provides a direct way to assess the expected five-year survival status of CRC patients during the perioperative period. For CRC patients, after fully assessing their cardiopulmonary function, first-stage R0 radical resection of the lesion has become the clinical treatment of choice. The postoperative management of comprehensive treatment may vary across centers. This study retrospectively collected CRC surgical cases from our center in 2019. By strict inclusion and exclusion criteria, patients who underwent neoadjuvant therapy or had preoperative liver metastasis were excluded, and only patients who underwent first-stage surgery with XELOX chemotherapy were included.

By integrating radiomics data with traditional patient demographics and TNM staging data, and applying dimensionality reduction and machine learning methods, this study provides a visualized model for predicting CRC patient survival within five years. Notably, we discovered that the predictive efficacy of pure radiomics data was not inferior to that of TNM staging alone, and the combination of both resulted in an even better prognosis prediction model. This offers a more comprehensive prediction of patient survival over the next 1, 3, or even 5 years. Thus, our study fills the gap where preoperative CT lacks quantitative guidance for assessing CRC patient prognosis, providing multidimensional data support for personalized treatment adjustments.

The prognostic improvement of the predictive model proposed in this study, compared to the conventional TNM staging system, is primarily reflected in the following aspects: First, based on data from our center and external validation cohorts, repeated analyses demonstrated that the AUC values of radiomics feature-based indicators showed no statistically significant difference from those of the TNM staging system alone, and even slightly outperformed the latter. This indicates that the proposed model, utilizing quantitative imaging biomarkers, can provide independent prognostic predictive capability. Second, compared to the inherent delay in acquiring traditional TNM staging data—which requires postoperative pathological examination after radical surgery to obtain a complete and relatively accurate staging—radiomics data can be accurately obtained prior to surgical intervention. This allows for earlier estimation of disease stage, offering strong clinical guidance for personalized treatment planning, particularly in low-income populations from underdeveloped regions, where decisions regarding aggressive systemic therapies must be carefully weighed. Finally, accurate TNM staging strictly depends on patients undergoing standard radical surgical procedures, as only then can a reliable pathological stage be determined. However, in real-world practice, the surgeon’s technical proficiency may limit the extent of lymph node dissection, potentially compromising the radicality of resection, which critically impacts pathological staging accuracy. In contrast, radiomics data can be objectively and reproducibly acquired preoperatively, thereby providing a novel complementary dimension to the conventional pathological staging system.

The prognostic prediction model in this study was constructed through a multidimensional approach integrating radiomic features, conventional TNM staging data, and relevant serological biomarkers, with patients’ actual survival time serving as the study endpoint. In collaboration with three hospitals, our research team collected external validation cohort data and performed corresponding external validation. The model demonstrated an AUC of 0.814, showing no significant deviation from the original study data (p>0.05), thereby confirming its robust predictive performance. Notably, as previously reported, the radiomic signature alone achieved an AUC of 0.807, which was not inferior to the AUC of 0.751 obtained using TNM staging alone (NRI=0.460, p<0.01; IDI=0.217, p<0.05). In the external validation cohort, TNM staging alone yielded an AUC of 0.750. These results collectively demonstrate that our model provides a universally applicable tool for prognostic prediction in colorectal cancer patients.

However, there are limitations to this study. Texture analysis relies on accurate annotation of the regions of interest (ROIs), which requires experienced clinicians or senior radiologists, and cannot yet fully automate prognosis prediction. In the actual practice of selecting accurate ROIs (Regions of Interest), significant subjective factors remain. To address this, our research team employed two board-certified radiologists, each with over ten years of experience, to independently delineate the ROIs. In cases of substantial disagreement, a third senior radiologist (similarly qualified with ≥10 years’ experience) was consulted for arbitration. This triple-validation protocol effectively minimizes measurement variability attributable to manual segmentation. Furthermore, given that the sample size in this study is not large, further validation with larger sample sizes is needed, especially in patients with confirmed preoperative liver metastasis. After expanding the number of cases, further studies should be conducted. If possible, multi-center cohort studies could be initiated to further refine the CRC prognosis prediction model and provide stronger support for individualized treatment. By using the nomogram model to predict the 1-year, 3-year and 5-year survival rates of patients, we can estimate patients’ expected lifespan more intuitively. Although this decision is quite difficult, it provides more quantitative numerical references for the choice between active clinical treatment and conservative palliative treatment. As for the future application scenarios and practical application in clinical decision-making, further long-term clinical verification is needed to confirm them.