Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study

Radiomics to predict PNI in ESCC

OBJECTIVE

This study aimed to investigate whether contrast-enhanced computed tomography (CECT) based radiomics analysis could noninvasively predict the perineural invasion (PNI) in esophageal squamous cell carcinoma (ESCC).

METHODS

398 patients with ESCC who underwent resection between February 2016 and March 2020 were retrospectively enrolled in this study. Patients were randomly divided into training and testing cohorts in a 7:3 ratio. Radiomics analysis was performed on the arterial phase images of CECT scans. From these images, 1595 radiomics features were initially extracted. The intraclass correlation coefficient (ICC), wilcoxon rank-sum test, spearman correlation analysis, and boruta algorithm were used for feature selection. Logistic regression (LR), random forest (RF), and support vector machine (SVM) models were established to preidict the PNI status. The performance of these radiomics models was assessed by the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was conducted to evaluate their clinical utility.

RESULTS

Six radiomics features were retained to build the radiomics models. Among these models, the random forest (RF) model demonstrated superior performance. In the training cohort, the AUC value of the RF model was 0.773, compared to 0.627 for the logistic regression (LR) model and 0.712 for the support vector machine (SVM) model. Similarly, in the testing cohort, the RF model achieved an AUC value of 0.767, outperforming the LR model at 0.638 and the SVM model at 0.683. Decision curve analysis (DCA) suggested that the RF radiomics model exhibited the highest clinical utility.

CONCLUSIONS

CECT-based radiomics analysis, particularly utilizing the RF, can noninvasively predict the PNI in ESCC preoperatively. This novel approach could enhance patient management by providing personalized information, thereby facilitating the development of individualized treatment strategies for ESCC patients.

Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis

BACKGROUND

Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for predicting PNI. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of radiomics models in predicting PNI in CRC.

METHODS

A comprehensive literature search was conducted across PubMed, Embase, and Web of Science for studies published up to July 28, 2024. Inclusion criteria focused on studies using radiomics models to predict PNI in CRC with sufficient data to construct diagnostic accuracy metrics. The quality of the included studies was assessed using QUADAS-2 and METRICS tools. Pooled estimates of sensitivity, specificity, and area under the curve (AUC) were calculated. Subgroup analyses were performed based on imaging modalities, segmentation methods, and other variables.

RESULTS

Twelve studies comprising 2853 patients were included in the systematic review, with ten studies contributing to the meta-analysis. The pooled sensitivity and specificity for radiomics models in predicting PNI were 0.74 (95% CI: 0.63-0.82) and 0.85 (95% CI: 0.79-0.90), respectively, in the training cohorts. In the validation cohorts, the sensitivity was 0.65 (95% CI: 0.57-0.72), and specificity was 0.85 (95% CI: 0.81-0.89). The AUC was 0.87 (95% CI: 0.63-0.82) for the training cohorts and 0.84 (95% CI: 0.81-0.87) for the validation cohorts, indicating good diagnostic accuracy. The METRICS scores for the included studies ranged from 65.8 to 85.1%, with an overall average score of 67.25%, reflecting good methodological quality. However, significant heterogeneity was observed across studies, particularly in sensitivity and specificity estimates.

CONCLUSION

Radiomics models show promise as a non-invasive tool for predicting PNI in CRC, with moderate to good diagnostic accuracy. However, the current study's limitations, including reliance on retrospective data, geographic concentration in China, and methodological variability, suggest that further research is needed. Future studies should focus on prospective designs, standardization of methodologies, and the integration of advanced machine-learning techniques to improve the clinical applicability and reliability of radiomics models in CRC management.

A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography

BACKGROUND

Colon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effective predictor of stage II-III colon cancer. Nonetheless, the lack of effective and facile predictive methodologies for detecting PNI prior operation in colon cancer remains a persistent challenge.

METHOD

Pre-operative computer tomography (CT) images and clinical data of patients diagnosed with stage II-III colon cancer between January 2015 and December 2023 were obtained from two sub-districts of Sun Yat-sen Memorial Hospital (SYSUMH). The LASSO/RF/PCA filters were used to screen radiomics features and LR/SVM models were utilized to construct radiomics model. A comprehensive model, shown as nomogram finally, combining with radiomics score and significant clinical features were developed and validated by area under the curve (AUC) and decision curve analysis (DCA).

RESULT

The total cohort, comprising 426 individuals, was randomly divided into a development cohort and a validation cohort as a 7:3 ratio. Radiomics scores were extracted from LASSO-SVM models with AUC of 0.898/0.726 in the development and validation cohorts, respectively. Significant clinical features (CA199, CA125, T-stage, and N-stage) were used to establish combining model with radiomics scores. The combined model exhibited superior reliability compared to single radiomics model in AUC value (0.792 vs. 0.726, p = 0.003) in validation cohorts. The radiomics-clinical model demonstrated an AUC of 0.918/0.792, a sensitivity of 0.907/0.813 and a specificity of 0.804/0.716 in the development and validation cohorts, respectively.

CONCLUSION

The study developed and validated a predictive nomogram model combining radiomics scores and clinical features, and showed good performance in predicting PNI pre-operation in stage II-III colon cancer patients.

Computed tomography-based radiomics nomogram for prediction of lympho-vascular and perineural invasion in esophageal squamous cell cancer patients: a retrospective cohort study

PURPOSE

Lympho-vascular invasion (LVI) and perineural invasion (PNI) have been established as prognostic factors in various types of cancers. The preoperative prediction of LVI and PNI has the potential to guide personalized medicine strategies for patients with esophageal squamous cell cancer (ESCC). This study investigates whether radiomics features derived from preoperative contrast-enhanced CT could predict LVI and PNI in ESCC patients.

METHODS AND MATERIALS

A retrospective cohort of 544 ESCC patients who underwent esophagectomy were included in this study. Preoperative contrast-enhanced CT images, pathological results of PNI and LVI, and clinical characteristics were collected. For each patient, the gross tumor volume (GTV-T) and lymph nodes volume (GTV-N) were delineated and four categories of radiomics features (first-order, shape, textural and wavelet) were extracted from GTV-T and GTV-N. The Mann-Whitney U test was used to select significant features associated with LVI and PNI in turn. Subsequently, radiomics signatures for LVI and PNI were constructed using LASSO regression with ten-fold cross-validation. Significant clinical characteristics were combined with radiomics signature to develop two nomogram models for predicting LVI and PNI, respectively. The area under the curve (AUC) and calibration curve were used to evaluate the predictive performance of the models.

RESULTS

The radiomics signature for LVI prediction consisted of 28 features, while the PNI radiomics signature comprised 14 features. The AUCs of the LVI radiomics signature were 0.77 and 0.74 in the training and validation groups, respectively, while the AUCs of the PNI radiomics signature were 0.69 and 0.68 in the training and validation groups. The nomograms incorporating radiomics signatures and significant clinical characteristics such as age, gender, thrombin time and D-Dimer showed improved predictive performance for both LVI (AUC: 0.82 and 0.80 in the training and validation group) and PNI (AUC: 0.75 and 0.72 in the training and validation groups) compared to the radiomics signature alone.

CONCLUSION

The radiomics features extracted from preoperative contrast-enhanced CT of gross tumor and lymph nodes have demonstrated their potential in predicting LVI and PNI in ESCC patients. Furthermore, the incorporation of clinical characteristics has shown additional value, resulting in improved predictive performance.

Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study

BACKGROUND

The presence of perineural invasion (PNI) in patients with rectal cancer (RC) is associated with significantly poorer outcomes. However, traditional diagnostic modalities have many limitations.

AIM

To develop a deep learning radiomics stacking nomogram model to predict preoperative PNI status in patients with RC.

METHODS

We recruited 303 RC patients and separated them into the training (n = 242) and test (n = 61) datasets on an 8: 2 scale. A substantial number of deep learning and hand-crafted radiomics features of primary tumors were extracted from the arterial and venous phases of computed tomography (CT) images. Four machine learning models were used to predict PNI status in RC patients: support vector machine, k-nearest neighbor, logistic regression, and multilayer perceptron. The stacking nomogram was created by combining optimal machine learning models for the arterial and venous phases with predicting clinical variables.

RESULTS

With an area under the curve (AUC) of 0.964 [95% confidence interval (CI): 0.944-0.983] in the training dataset and an AUC of 0.955 (95%CI: 0.900-0.999) in the test dataset, the stacking nomogram demonstrated strong performance in predicting PNI status. In the training dataset, the AUC of the stacking nomogram was greater than that of the arterial support vector machine (ASVM), venous SVM, and CT-T stage models (P < 0.05). Although the AUC of the stacking nomogram was greater than that of the ASVM in the test dataset, the difference was not particularly noticeable (P = 0.05137).

CONCLUSION

The developed deep learning radiomics stacking nomogram was effective in predicting preoperative PNI status in RC patients.

Quantitative parameters of dual-layer spectral detector computed tomography for evaluating differentiation grade and lymphovascular and perineural invasion in colorectal adenocarcinoma

PURPOSE

To explore the predictive value of dual-layer spectral detector CT (SDCT) quantitative parameters for determining differentiation grade, lymphovascular invasion (LVI) and perineural invasion (PNI) in colorectal adenocarcinoma (CRAC) patients.

METHODS

A total of 106 eligible patients with CRAC were included in this study. Spectral parameters, including CT values at 40 and 100 keV, the effective atomic number (Zeff), the iodine concentration (IC), the slope of the spectral Hounsfield unit (HU) curve (λHU), and the normalized iodine concentration (NIC) in the arterial phase (AP) and venous phase (VP), were compared according to the differentiation grade and the status of LVI and PNI. The diagnostic accuracies of the quantitative parameters with statistical significance were determined via receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated.

RESULTS

There were 57 males and 49 females aged 43-86 (69 ± 10) years. The measured values of the spectral quantitative parameters of the CRAC were consistent within the observer (ICC range: 0.800-0.926). The 40 keV-AP, IC-AP, NIC-AP, 40 keV-VP, and IC-VP were significantly different among the different differentiation grades in the CRAC (P = 0.040, AUC = 0.673; P = 0.035, AUC = 0.684; P = 0.031, AUC = 0.639; P = 0.044, AUC = 0.663 and P = 0.035, AUC = 0.666, respectively). A statistically significant difference was observed in 40 keV-VP, 100 keV-VP, Zeff-VP, IC-VP, and λHU-VP between LVI-positive and LVI-negative patients (P = 0.003, AUC = 0.688; P = 0.015, AUC = 0.644; P = 0.001, AUC = 0.688; P = 0.001, AUC = 0.703 and P = 0.003, AUC = 0.677, respectively). There were no statistically significant differences in the values of the spectral parameters of the PNI state of patients with CRAC (P > 0.05).

CONCLUSION

The quantitative parameters of SDCT had good diagnostic efficacy in differentiating between different grades and statuses of LVI in patients with CRAC; however, SDCT did not have value for identifying the state of PNI.

Spectral CT in the evaluation of perineural invasion status in rectal cancer

PURPOSE

To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer.

METHODS

Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70 keV), spectral curve slope (K(HU)), effective atomic number (Zeff), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test.

RESULTS

The preoperative spectral CT parameters (40-70 keV attenuation, K(HU), Zeff, and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%.

CONCLUSION

Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency.

Application research of radiomics in colorectal cancer: A bibliometric study

BACKGROUND

Radiomics has shown great potential in the clinical field of colorectal cancer (CRC). However, few bibliometric studies have systematically analyzed existing research in this field. The purpose of this study is to understand the current research status and future development directions of CRC.

METHODS

Search the English documents on the application of radiomics in the field of CRC research included in the Web of Science Core Collection from its establishment to October 2023. VOSviewer and CiteSpace software were used to conduct bibliometric and visual analysis of online publications related to countries/regions, authors, journals, references, and keywords in this field.

RESULTS

A total of 735 relevant documents published from Web of Science Core Collection to October 2023 were retrieved, and a total of 419 documents were obtained based on the screening criteria, including 376 articles and 43 reviews. The number of publications is increasing year by year. Among them, China publishes the most relevant documents (n = 238), which is much higher than Italy (n = 69) and the United States (n = 63). Tian Jie is the author with the most publications and citations (n = 17, citations = 2128), GE Healthcare is the most productive institution (n = 26), Frontiers in Oncology is the journal with the most publications (n = 60), and European Radiology is the most cited journal (n = 776). Hot spots for the application of radiomics in CRC include magnetic resonance, neoadjuvant chemoradiotherapy, survival, texture analysis, and machine learning. These directions are the current hot spots for the application of radiomics research in CRC and may be the direction of continued development in the future.

CONCLUSION

Through bibliometric analysis, the application of radiomics in CRC has been increasing year by year. The application of radiomics improves the accuracy of preoperative diagnosis, prediction, and prognosis of CRC. The results of bibliometrics analysis provide a valuable reference for the research direction of radiomics. However, radiomics still faces many challenges in the future, such as the single nature of the data source which may affect the comprehensiveness of the results. Future studies can further expand the data sources and build a multicenter public database to more comprehensively reflect the research status and development trend of CRC radiomics.

The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study

BACKGROUND

We aimed to explore the application value of various machine learning (ML) algorithms based on multicenter CT radiomics in identifying peripheral nerve invasion (PNI) of colorectal cancer (CRC).

METHODS

A total of 268 patients with colorectal cancer who underwent CT examination in two hospitals from January 2016 to December 2022 were considered. Imaging and clinicopathological data were collected through the Picture Archiving and Communication System (PACS). The Feature Explorer software (FAE) was used to identify the peripheral nerve invasion of colorectal patients in center 1, and the best feature selection and classification channels were selected. Finally, the best feature selection and classifier pipeline were verified in center 2.

RESULTS

The six-feature models using RFE feature selection and GP classifier had the highest AUC values, which were 0.610, 0.699, and 0.640, respectively. FAE generated a more concise model based on one feature (wavelet-HLL-glszm-LargeAreaHighGrayLevelEmphasis) and achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively, using the "one standard error" rule. Using ANOVA feature selection, the GP classifier had the best AUC value in a one-feature model, with AUC values of 0.611, 0.663, and 0.643 on the validation, internal test, and external test sets, respectively. Similarly, when using the "one standard error" rule, the model based on one feature (wave-let-HLL-glszm-LargeAreaHighGrayLevelEmphasis) achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively.

CONCLUSIONS

Combining artificial intelligence and radiomics features is a promising approach for identifying peripheral nerve invasion in colorectal cancer. This innovative technique holds significant potential for clinical medicine, offering broader application prospects in the field.

CRITICAL RELEVANCE STATEMENT

The multi-channel ML method based on CT radiomics has a simple operation process and can be used to assist in the clinical screening of patients with CRC accompanied by PNI.

KEY POINTS

• Multi-channel ML in the identification of peripheral nerve invasion in CRC. • Multi-channel ML method based on CT-radiomics can detect the PNI of CRC. • Early preoperative identification of PNI in CRC is helpful to improve the formulation of treatment strategies and the prognosis of patients.

Preoperative Prediction of Perineural Invasion in Oesophageal Squamous Cell Carcinoma Based on CT Radiomics Nomogram: A Multicenter Study

RATIONALE AND OBJECTIVES

To investigate the value of computed tomography (CT) radiomics nomogram in the preoperative prediction of perineural invasion (PNI) in oesophageal squamous cell carcinoma (ESCC) through a multicenter study.

MATERIALS AND METHODS

We retrospectively collected postoperative pathological data of 360 ESCC patients with definite PNI status (131 PNI-positive and 229 PNI-negative) from two centres. Radiomic features were extracted from the arterial-phase CT images, and the least absolute shrinkage and selection operator and logistic regression algorithm were used to screen valuable features for identifying the PNI status and calculating the radiomics score (Rad-score). A radiomics nomogram was established by integrating the Rad-score and clinical risk factors. A receiver operating characteristic curve was used to evaluate model performance, and decision curve analysis was used to evaluate the predictive performance of the radiomics nomogram in the training, internal validation, and external validation sets.

RESULTS

Twenty radiomics features were extracted from a full-volume tumour region of interest to construct the model, and the radiomics nomogram combined with radiomics features and clinical risk factors was superior to the clinical and radiomics models in predicting the PNI status of ESCC patients. The area under the curve values of the radiomics nomogram in the training, internal validation, and external validation sets were 0.856 (0.794-0.918), 0.832 (0.742-0.922), and 0.803 (0.709-0.898), respectively.

CONCLUSION

The radiomics nomogram based on CT has excellent predictive ability; it can non-invasively predict the preoperative PNI status of ESCC patients and provide a basis for preoperative decision-making.

Development and validation of a combined nomogram for predicting perineural invasion status in rectal cancer via computed tomography-based radiomics

AIM

This study aimed to create and validate a clinic-radiomics nomogram based on computed tomography (CT) imaging for predicting preoperative perineural invasion (PNI) of rectal cancer (RC).

MATERIAL AND METHODS

This study enrolled 303 patients with RC who were divided into training (n = 242) and test datasets (n = 61) in an 8:2 ratio with all their clinical outcomes. A total of 3,296 radiomic features were extracted from CT images. Five machine learning (ML) models (logistic regression (LR)/K-nearest neighbor (KNN)/multilayer perceptron (MLP)/support vector machine (SVM)/light gradient boosting machine (LightGBM)) were developed using radiomic features derived from the arterial and venous phase images, and the model with the best diagnostic performance was selected. By combining the radiomics and clinical signatures, a fused nomogram model was constructed.

RESULTS

After using the Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) to remove redundant features, the MLP model proved to be the most efficient among the five ML models. The fusion nomogram based on MLP prediction probability further improves the ability to predict the PNI status. The area under the curve (AUC) of the training and test sets was 0.883 and 0.889, respectively, which were higher than those of the clinical (training set, AUC = 0.710; test set, AUC = 0.762) and radiomic models (training set, AUC = 0.840; test set, AUC = 0.834).

CONCLUSIONS

The clinical-radiomics combined nomogram model based on enhanced CT images efficiently predicted the PNI status of patients with RC.