Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer

Prediction of Tumor Budding Grading in Rectal Cancer Using a Multiparametric MRI Radiomics Combined with a 3D Vision Transformer Deep Learning Approach

RATIONALE AND OBJECTIVES

The objective is to assess the effectiveness of a multiparametric MRI radiomics strategy combined with a 3D Vision Transformer (ViT) deep learning (DL) model in predicting tumor budding (TB) grading in individuals diagnosed with rectal cancer (RC).

MATERIALS AND METHODS

This retrospective study analyzed data from 349 patients diagnosed with rectal adenocarcinoma across two hospitals. A total of 267 patients from our institution were randomly allocated to a training cohort (n=187) or an internal test cohort (n=80) in a 7:3 ratio. Furthermore, a cohort of 82 patients from another hospital was established for external testing purposes. Univariate and multivariate analyses were performed to pinpoint independent clinical risk factors, which were then utilized to develop a clinical model. Radiomics (Rad) models, a 3D ViT DL model, and a combined model (DLR) were built using 3D T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1CE). The evaluation of each model's predictive performance involved calculating the area under the curve (AUC), conducting the Delong test, and examining calibration curves alongside decision curve analysis (DCA).

RESULTS

No notable clinical characteristics were observed in either univariate or multivariate analyses, hindering the establishment of a clinical model. The DLR model demonstrated exceptional performance, attaining an AUC of 0.938 (95% CI: 0.906-0.969) within the training cohort, 0.867 (95% CI: 0.779-0.954) in the internal test cohort, and 0.824 (95% CI: 0.734-0.914) in the external test cohort.

CONCLUSION

The combination of multiparametric MRI radiomics and 3D ViT DL effectively and non-invasively predicts TB grading in RC patients, offering valuable insights for personalized treatment planning and prognosis assessment.

Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retrospectively collected data from 108 patients with pathologically confirmed rectal adenocarcinoma who underwent preoperative multiparametric MRI at the First Affiliated Hospital of Bengbu Medical College between April 2019 and August 2023. This dataset was subsequently divided into training and validation sets following a ratio of 7:3. Both univariate and multivariate logistic regression analyses were implemented to identify independent clinical risk factors associated with perineural invasion (PNI) in rectal cancer. We manually delineated the region of interest (ROI) layer-by-layer on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences and extracted the image features. Five machine learning algorithms were used to construct radiomics model with the features selected by least absolute shrinkage and selection operator (LASSO) method. The optimal radiomics model was then selected and combined with clinical features to formulate a nomogram model. The model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and its clinical value was assessed via decision curve analysis (DCA). Our final selection comprised 10 optimal radiological features and the SVM model showcased superior predictive efficiency and robustness among the five classifiers. The area under the curve (AUC) values of the nomogram model were 0.945 (0.899, 0.991) and 0.846 (0.703, 0.99) for the training and validation sets, respectively. The nomogram model developed in this study exhibited excellent predictive performance in foretelling PNI of rectal cancer, thereby offering valuable guidance for clinical decision-making. The nomogram could predict the perineural invasion status of rectal cancer in early stage.

Risk factors influencing sphincter preservation in laparoscopic radical rectal cancer surgery

BACKGROUND

The surgical management of rectal cancer is continuously advancing, with a current emphasis on minimising the need for a permanent stoma. Understanding the risk factors influencing sphincter preservation is crucial for guiding clinical decision-making and optimising preoperative patient evaluation.

AIM

To examine the risk factors influencing sphincter preservation in laparoscopic radical rectal cancer surgery.

METHODS

A retrospective analysis of the demographics, preoperative and intraoperative data, and pathological findings of 179 patients with rectal cancer who underwent laparoscopic radical rectal cancer surgery at our hospital between January 2022 and December 2023 was conducted. These clinical data were compared between two groups: Patients with sphincter preservation and those without, categorised as the sphincter-preserved and sphincter-unpreserved groups, respectively.

RESULTS

Of the 179 patients analysed, 150 were in the sphincter-preserved group and 29 were in the sphincter-unpreserved group. Tumour height was significantly greater in the sphincter-preserved group compared to the sphincter-unpreserved group. Conversely, elevated levels of carcinoembryonic antigen, carbohydrate antigen 19-9, and plasma D-dimer were significantly higher in the sphincter-unpreserved group. Significant differences were also observed between the two groups in terms of place of residence, presence of colonic polyps, neoadjuvant chemotherapy, preoperative radiotherapy, mucinous adenocarcinoma, nerve invasion, and tumour height. No significant differences were observed for other parameters. Logistic regression analysis identified colonic polyps, mucinous adenocarcinoma, nerve invasion, and tumour height as independent risk factors for sphincter preservation.

CONCLUSION

Several risk factors influencing sphincter preservation in laparoscopic radical rectal cancer surgery were identified. These factors could be valuable tools for guiding clinical decision-making and optimising preoperative patient evaluations.

A CT-based radiomics nomogram for the preoperative prediction of perineural invasion in pancreatic ductal adenocarcinoma

Purpose

To develop a nomogram based on CT radiomics features for preoperative prediction of perineural invasion (PNI) in pancreatic ductal adenocarcinoma (PDAC) patients.

Methods

A total of 217 patients with histologically confirmed PDAC were enrolled in this retrospective study. Radiomics features were extracted from the whole tumor. Univariate analysis, least absolute shrinkage and selection operator and logistic regression were applied for feature selection and radiomics model construction. Finally, a nomogram combining the radiomics score (Rad-score) and clinical characteristics was established. Receiver operating characteristic curve analysis, calibration curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the nomogram.

Results

According to multivariate analysis, CT features, including the radiologists evaluated PNI status based on CECT (CTPNI) (OR=1.971 [95% CI: 1.165, 3.332], P=0.01), the lymph node status determined on CECT (CTLN) (OR=2.506 [95%: 1.416, 4.333], P=0.001) and the Rad-score (OR=3.666 [95% CI: 2.069, 6.494], P<0.001), were significantly associated with PNI. The area under the receiver operating characteristic curve (AUC) for the nomogram combined with the Rad-score, CTLN and CTPNI achieved favorable discrimination of PNI status, with AUCs of 0.846 and 0.778 in the training and testing cohorts, respectively, which were superior to those of the Rad-score (AUC of 0.720 in the training cohort and 0.640 in the testing cohort) and CTPNI (AUC of 0.610 in the training cohort and 0.675 in the testing cohort). The calibration plot and decision curve showed good results.

Conclusion

The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with PDAC.

Amide proton transfer-weighted MRI for assessing rectal adenocarcinoma T-staging and perineural invasion: a prospective study

OBJECTIVE

To investigate the value of the pre-operative amide proton transfer-weighted (APTw) MRI to assess the prognostic factors in rectal adenocarcinoma (RA).

METHODS

This prospective study ran from January 2022 to September 2023 and consecutively enrolled participants with RA who underwent pre-operative MRI and radical surgery. The APTw signal intensity (SI) values of RA with various tumor (T), node (N) stages, perineural invasion (PNI), and tumor grade were compared by Mann-Whitney U-test or t-test. The receiver operating characteristic curve was used to evaluate the diagnostic performance of the APTw SI values.

RESULTS

A total of 51 participants were enrolled (mean age, 58 years ± 10 [standard deviation], 26 men). There were 24 in the T1-T2 stage and 9 with positive PNI. The APTw SI max, 99th, and 95th values were significantly higher in T3-T4 stage tumor than in T1-T2; the median (interquartile range) (M (IQR)) was (4.0% (3.6-4.9%) vs 3.4% (2.9- 4.3%), p = 0.017), (3.7% (3.2-4.1%) vs 3.2% (2.8-3.8%), p = 0.013), and (3.3% (2.8-3.8%) vs 2.9% (2.3-3.5%), p = 0.033), respectively. These indicators also differed significantly between the PNI groups, with the M (IQR) (4.5% (3.6-5.7%) vs 3.7% (3.2-4.2%), p = 0.017), (4.1% (3.4-4.8%) vs 3.3% (3.0-3.9%), p = 0.022), and (3.7% (2.7-4.2%) vs 2.9% (2.6-3.5%), p = 0.045), respectively.

CONCLUSION

Pre-operative APTw MRI has potential value in the assessment of T-staging and PNI determination in RA.

CLINICAL RELEVANCE STATEMENT

Pre-operative amide proton transfer-weighted MRI provides a quantitative method for noninvasive assessment of T-staging and PNI in RA aiding in precision treatment planning.

KEY POINTS

The efficacy of APTw MRI in RA needs further investigation. T3-T4 stage and PNI positive APTw signal intensities were higher than T1-T2 and non-PNI, respectively. APTw MRI provides a quantitative method for assessment of T staging and PNI in RA.

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.

The accuracy of radiomics in diagnosing tumor deposits and perineural invasion in rectal cancer: a systematic review and meta-analysis

Background

Radiomics has emerged as a promising approach for diagnosing, treating, and evaluating the prognosis of various diseases in recent years. Some investigators have utilized radiomics to create preoperative diagnostic models for tumor deposits (TDs) and perineural invasion (PNI) in rectal cancer (RC). However, there is currently a lack of comprehensive, evidence-based support for the diagnostic performance of these models. Thus, the accuracy of radiomic models was assessed in diagnosing preoperative RC TDs and PNI in this study.

Methods

PubMed, EMBASE, Web of Science, and Cochrane Library were searched for relevant articles from their establishment up to December 11, 2023. The radiomics quality score (RQS) was used to evaluate the risk of bias in the methodological quality and research level of the included studies.

Results

This meta-analysis included 15 eligible studies, most of which employed logistic regression models (LRMs). For diagnosing TDs, the c-index, sensitivity, and specificity of models based on radiomic features (RFs) alone were 0.85 (95% CI: 0.79 - 0.90), 0.85 (95% CI: 0.75 - 0.91), and 0.82 (95% CI: 0.70 - 0.89); in the validation set, the c-index, sensitivity, and specificity of models based on both RFs and interpretable CFs were 0.87 (95% CI: 0.83 - 0.91), 0.91 (95% CI: 0.72 - 0.99), and 0.65 (95% CI: 0.53 - 0.76), respectively. For diagnosing PNI, the c-index, sensitivity, and specificity of models based on RFs alone were 0.80 (95% CI: 0.74 - 0.86), 0.64 (95% CI: 0.44 - 0.80), and 0.79 (95% CI: 0.68 - 0.87) in the validation set; in the validation set, the c-index, sensitivity, and specificity of models based on both RFs and interpretable CFs were 0.83 (95% CI: 0.77 - 0.89), 0.60 (95% CI: 0.48 - 0.71), and 0.90 (95% CI: 0.84 - 0.94), respectively.

Conclusions

Diagnostic models based on both RFs and CFs have proven effective in preoperatively diagnosing TDs and PNI in RC. This non-invasive method shows promise as a new approach.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=498660, identifier CRD42024498660.

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.

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.

Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3

BACKGROUND

This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer.

METHODS

We included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data.

RESULTS

After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the trainingset and 0.920 in the test set.

CONCLUSION

The study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study

BACKGROUND

Perineural invasion (PNI) has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer (RC). Preoperative prediction of PNI status is helpful for individualized treatment of RC. Recently, several radiomics studies have been used to predict the PNI status in RC, demonstrating a good predictive effect, but the results lacked generalizability. The preoperative prediction of PNI status is still challenging and needs further study.

AIM

To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.

METHODS

This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers. The patients underwent pre-operative high-resolution magnetic resonance imaging (MRI) between May 2019 and August 2022. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI) and contrast-enhanced T1WI (T1CE) sequences. The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared (T2WI, T1CE and T2WI + T1CE fusion sequences). A clinical-radiomics (CR) model was established by combining the radiomics features and clinical risk factors. The internal and external validation groups were used to validate the proposed models. The area under the receiver operating characteristic curve (AUC), DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the model performance.

RESULTS

Among the radiomics models, the T2WI + T1CE fusion sequences model showed the best predictive performance, in the training and internal validation groups, the AUCs of the fusion sequence model were 0.839 [95% confidence interval (CI): 0.757-0.921] and 0.787 (95%CI: 0.650-0.923), which were higher than those of the T2WI and T1CE sequence models. The CR model constructed by combining clinical risk factors had the best predictive performance. In the training and internal and external validation groups, the AUCs of the CR model were 0.889 (95%CI: 0.824-0.954), 0.889 (95%CI: 0.803-0.976) and 0.894 (95%CI: 0.814-0.974). Delong test, NRI, and IDI showed that the CR model had significant differences from other models (P < 0.05). Calibration curves demonstrated good agreement, and DCA revealed significant benefits of the CR model.

CONCLUSION

The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively, which facilitates individualized treatment of RC patients.

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.

The Role of Radiomics in Rectal Cancer

PURPOSE

Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT.

METHODS

We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically.

RESULTS

The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice.

CONCLUSION

Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.

Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics

Objective

This study aimed to explore the radiomics model based on magnetic resonance imaging (MRI) T2WI and compare the value of different machine algorithms in preoperatively predicting tumor budding (TB) grading in rectal cancer.

Methods

A retrospective study was conducted on 266 patients with preoperative rectal MRI examinations, who underwent complete surgical resection and confirmed pathological diagnosis of rectal cancer. Among them, patients from Qingdao West Coast Hospital were assigned as the training group (n=172), while patients from other hospitals were assigned as the external validation group (n=94). Regions of interest (ROIs) were delineated, and image features were extracted and dimensionally reduced using the Least Absolute Shrinkage and Selection Operator (LASSO). Eight machine algorithms were used to construct the models, and the diagnostic performance of the models was evaluated and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as clinical utility assessment using decision curve analysis (DCA).

Results

A total of 1197 features were extracted, and after feature selection and dimension reduction, 11 image features related to TB grading were obtained. Among the eight algorithm models, the support vector machine (SVM) algorithm achieved the best diagnostic performance, with accuracy, sensitivity, and specificity of 0.826, 0.949, and 0.723 in the training group, and 0.713, 0.579, and 0.804 in the validation group, respectively. DCA demonstrated the clinical utility of this radiomics model.

Conclusion

The radiomics model based on MR T2WI can provide an effective and noninvasive method for preoperative TB grading assessment in patients with rectal cancer.

A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer

RATIONALE AND OBJECTIVES

To establish a prediction model for the efficacy of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), using pretreatment magnetic resonance imaging (MRI) multisequence image features and clinical parameters.

MATERIALS AND METHODS

Patients with clinicopathologically confirmed LARC were included (training and validation datasets, n = 100 and 27, respectively). Clinical data of patients were collected retrospectively. We analyzed MRI multisequence imaging features. The tumor regression grading (TRG) system proposed by Mandard et al was adopted. Grade 1-2 of TRG was a good response group, and grade 3-5 of TRG was a poor response group. In this study, a clinical model, a single sequence imaging model, and a comprehensive model combined with clinical imaging were constructed, respectively. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive efficacy of clinical, imaging, and comprehensive models. The decision curve analysis method evaluated the clinical benefit of several models, and the nomogram of efficacy prediction was constructed.

RESULTS

The AUC value of the comprehensive prediction model is 0.99 in the training data set and 0.94 in the test data set, which is significantly higher than other models. Radiomic Nomo charts were developed using Rad scores obtained from the integrated image omics model, circumferential resection margin(CRM), DoTD, and carcinoembryonic antigen(CEA). Nomo charts showed good resolution. The calibrating and discriminating ability of the synthetic prediction model is better than that of the single clinical model and the single sequence clinical image omics fusion model.

CONCLUSION

Nomograph, based on pretreatment MRI characteristics and clinical risk factors, has the potential to be used as a noninvasive tool to predict outcomes in patients with LARC after nCRT.

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.

Preoperative prediction of microvascular invasion and perineural invasion in pancreatic ductal adenocarcinoma with 18F-FDG PET/CT radiomics analysis

AIM

To develop and validate a predictive model based on 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (¹⁸F-FDG) positron-emission tomography (PET)/computed tomography (CT) radiomics features and clinicopathological parameters to preoperatively identify microvascular invasion (MVI) and perineural invasion (PNI), which are important predictors of poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Preoperative ¹⁸F-FDG PET/CT images and clinicopathological parameters of 170 patients in PDAC were collected retrospectively. The whole tumour and its peritumoural variants (tumour dilated with 3, 5, and 10 mm pixels) were applied to add tumour periphery information. A feature-selection algorithm was employed to mine mono-modality and fused feature subsets, then conducted binary classification using gradient boosted decision trees.

RESULTS

For MVI prediction, the model performed best on a fused subset of ¹⁸F-FDG PET/CT radiomics features and two clinicopathological parameters, with an area under the receiver operating characteristic curve (AUC) of 83.08%, accuracy of 78.82%, recall of 75.08%, precision of 75.5%, and F1-score of 74.59%. For PNI prediction, the model achieved best prediction results only on the subset of PET/CT radiomics features, with AUC of 94%, accuracy of 89.33%, recall of 90%, precision of 87.81%, and F1 score of 88.35%. In both models, 3 mm dilation on the tumour volume produced the best results.

CONCLUSIONS

The radiomics predictors from preoperative ¹⁸F-FDG PET/CT imaging exhibited instructive predictive efficacy in the identification of MVI and PNI status preoperatively in PDAC. Peritumoural information was shown to assist in MVI and PNI predictions.

Development and accuracy validation of a fat fraction imaging biomarker for sialadenitis in the parotid gland

BACKGROUND

The diagnosis of sialadenitis, the most frequent disease of the salivary glands, is challenging when the symptoms are mild. In such cases, biomarkers can be used as definitive diagnostic indicators. Recently, biomarkers have been developed by extracting and analyzing pathological and morphological features from medical imaging. This study aimed to establish a diagnostic reference for sialadenitis based on the quantitative magnetic resonance imaging (MRI) biomarker IDEAL-IQ and assess its accuracy.

METHODS

Patients with sialadenitis (n = 46) and control subjects (n = 90) that underwent MRI were selected. Considering that the IDEAL-IQ value is a sensitive fat fractional marker to the body mass index (BMI), all subjects were also categorized as under-, normal-, and overweight. The fat fraction of parotid gland in the control and sialadenitis groups were obtained using IDEAL-IQ map. The values from the subjects in the control and sialadenitis groups were compared in each BMI category. For comparison, t-tests and receiver operating characteristic (ROC) curve analyses were performed.

RESULTS

The IDEAL-IQ fat faction of the control and sialadenitis glands were 38.57% and 23.69%, respectively, and the differences were significant. The values were significantly lower in the sialadenitis group (P), regardless of the BMI types. The area under the ROC curve (AUC) was 0.83 (cut-off value: 28.72) in patients with sialadenitis. The AUC for under-, normal-, and overweight individuals were 0.78, 0.81, and 0.92, respectively.

CONCLUSIONS

The fat fraction marker based on the IDEAL-IQ method was useful as an objective indicator for diagnosing sialadenitis. This marker would aid less-experienced clinicians in diagnosing sialadenitis.

Preoperative Prediction of Perineural Invasion Status of Rectal Cancer Based on Radiomics Nomogram of Multiparametric Magnetic Resonance Imaging

Objectives

To compare the predictive performance of different radiomics signatures from multiparametric magnetic resonance imaging (mpMRI), including four sequences when used individually or combined, and to establish and validate an optimal nomogram for predicting perineural invasion (PNI) in rectal cancer (RC) patients.

Methods

Our retrospective study included 279 RC patients without preoperative antitumor therapy (194 in the training dataset and 85 in the test dataset) who underwent preoperative mpMRI scan between January 2017 and January 2021. Among them, 72 cases were PNI-positive. Then, clinical and radiological variables were collected, including carcinoembryonic antigen (CEA), radiological tumour stage (T_1-4), lymph node stage (N_0-2) and so on. Quantitative radiomics features were extracted and selected from oblique axial T₂-weighted imaging (T₂WI), T₁-weighted imaging (T₁WI), apparent diffusion coefficient (ADC), and enhanced T₁WI (T₁CE) sequences. The clinical model was constructed by integrating the final selected clinical and radiological variables. The radiomics signatures included four single-sequence signatures and one fusion signature were built using the respective remaining optimized features. And the nomogram was constructed based on the independent predictors by using multivariable logistic regression. The area under curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance.

Results

Ultimately, 20 radiomics features were retained from the four sequences—T₁WI (n = 4), T₂WI (n = 5), ADC (n = 5), and T₁CE (n = 6)—to construct four single-sequence radiomics signatures and one fusion radiomics signature. The fusion radiomics signature performed better than four single-sequence radiomics signatures and clinical model (AUCs of 0.835 and 0.773 vs. 0.680-0.737 and 0.666-0.709 in the training and test datasets, respectively). The nomogram constructed by incorporating CEA, tumour stage and rad-score performed best, with AUCs of 0.869 and 0.864 in the training and test datasets, respectively. Delong test showed that the nomogram was significantly different from the clinical model and four single-sequence radiomics signatures (P < 0.05). Moreover, calibration curves demonstrated good agreement, and DCA highlighted benefits of the nomogram.

Conclusions

The comprehensive nomogram can preoperatively and noninvasively predict PNI status, provide a convenient and practical tool for treatment strategy, and help optimize individualized clinical decision-making in RC patients.

The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer

PURPOSE

Perineural invasion (PNI) has been recognized as an important prognosis factor in patients with colorectal cancer (CRC). The purpose of this retrospective study was to investigate the value of ¹⁸F-FDG PET/CT-based radiomics integrating clinical information, PET/CT features, and metabolic parameters for preoperatively predicting PNI and outcome in non-metastatic CRC and establish an easy-to-use nomogram.

METHODS

A total of 131 patients with non-metastatic CRC who undergo PET/CT scan were retrospectively enrolled. Univariate analysis was used to compare the differences between PNI-present and PNI-absent groups. Multivariate logistic regression was performed to select the independent predictors for PNI status. Akaike information criterion (AIC) was used to select the best prediction models for PNI status. CT radiomics signatures (RSs) and PET-RSs were selected by maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) regression and radiomics scores (Rad-scores) were calculated for each patient. The prediction models with or without Rad-score were established. According to the nomogram, nomogram scores (Nomo-scores) were calculated for each patient. The performance of different models was assessed with the area under the curve (AUC), specificity, and sensitivity. The clinical usefulness was assessed by decision curve (DCA). Multivariate Cox regression was used to selected independent predictors of progression-free survival (PFS).

RESULTS

Among all the clinical information, PET/CT features, and metabolic parameters, CEA, lymph node metastatic on PET/CT (N stage), and total lesion glycolysis (TLG) were independent predictors for PNI (p < 0.05). Six CT-RSs and 12 PET-RSs were selected as the most valuable factors to predict PNI. The Rad-score calculated with these RSs was significantly different between PNI-present and PNI-absent groups (p < 0.001). The AUC of the constructed model was 0.90 (95%CI: 0.83-0.97) in the training cohort and 0.80 (95%CI: 0.65-0.95) in the test cohort. The nomogram's predicting sensitivity was 0.84 and the specificity was 0.83 in the training cohort. The clinical model's predicting sensitivity and specificity were 0.66 and 0.85 in the training cohort, respectively. Besides, DCA showed that patients with non-metastatic CRC could get more benefit with our model. The results also indicated that N stage, PNI status, and the Nomo-score were independent predictors of PFS in patients with non-metastatic CRC.

CONCLUSION

The nomogram, integrating clinical data, PET/CT features, metabolic parameters, and radiomics, performs well in predicting PNI status and is associated with the outcome in patients with non-metastatic CRC.

Radiomics for predicting perineural invasion status in rectal cancer

BACKGROUND

Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging.

AIM

To establish a radiomics model for evaluating PNI status preoperatively in RC patients.

METHODS

This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection (n = 6) and machine-learning (n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).

RESULTS

One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved (P < 0.001 in the training cohort, and P = 0.045 in the validation cohort).

CONCLUSION

The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Emerging applications of radiomics in rectal cancer: State of the art and future perspectives

Rectal cancer (RC) is the third most commonly diagnosed cancer and has a high risk of mortality, although overall survival rates have improved. Preoperative assessments and predictions, including risk stratification, responses to therapy, long-term clinical outcomes, and gene mutation status, are crucial to guide the optimization of personalized treatment strategies. Radiomics is a novel approach that enables the evaluation of the heterogeneity and biological behavior of tumors by quantitative extraction of features from medical imaging. As these extracted features cannot be captured by visual inspection, the field holds significant promise. Recent studies have proved the rapid development of radiomics and validated its diagnostic and predictive efficacy. Nonetheless, existing radiomics research on RC is highly heterogeneous due to challenges in workflow standardization and limitations of objective cohort conditions. Here, we present a summary of existing research based on computed tomography and magnetic resonance imaging. We highlight the most salient issues in the field of radiomics and analyze the most urgent problems that require resolution. Our review provides a cutting-edge view of the use of radiomics to detect and evaluate RC, and will benefit researchers dedicated to using this state-of-the-art technology in the era of precision medicine.