MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Development of a joint prediction model based on both the radiomics and clinical factors for preoperative prediction of circumferential resection margin in middle-low rectal cancer using T2WI images

OBJECTIVES

A circumferential resection margin (CRM) is an independent risk factor for local recurrence, distant metastasis, and poor overall survival of rectal cancer. In this study, we developed and validated a radiomics prediction model to predict perioperative surgical margins in patients with middle and low rectal cancer following neoadjuvant treatment and for decisions about treatment plans for patients.

METHODS

This study retrospectively analyzed 275 patients from center 1(training cohort) and 120 patients from center 2(verification cohort) with rectal cancer diagnosed at two centers from July 2020 to July 2022 who underwent neoadjuvant therapy and had their CRM status confirmed by preoperative high-resolution magnetic resonance imaging (MRI) scans. Radiomics signatures were extracted and screened from MRI images and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model, which was combined with clinical signatures to construct a nomogram. The receiver operating characteristic (ROC) curve and area under the curve (AUC) value, sensitivity, specificity, positive predictive value, negative predictive value, and calibration curve were used to evaluate the predictive performance of the model.

RESULTS

In our research, the combined model has the best performance. In the training group, the radiomics model based on high-spatial-resolution T2-weighted imaging (HR-T2WI), clinical model and combined model demonstrated an AUC of 0.819 (0.802-0.833), 0.843 (0.822-0.861), and 0.910 (0.880-0.940), respectively. In the validation group, they demonstrated an AUC of 0.745 (0.715-0.788), 0.827 (0.798-0.850), and 0.848 (0.779-0.917), respectively. The calibration curve confirmed the clinical applicability of the model.

CONCLUSIONS

The individualized prediction model established by combining radiomics signatures and clinical signatures can efficiently and objectively predict perioperative margin invasion in patients with middle and low rectal cancer.

Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review

PURPOSE

The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat-ment strategies for patients with rectal cancer.

METHODS

This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation.

RESULTS

AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer-ing noninvasive and cost-effective alternatives for identifying genetic mutations.

CONCLUSION

Image-based AI studies for rectal can-cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

Ensemble Machine Learning Model Incorporating Radiomics and Body Composition for Predicting Intraoperative HDI in PPGL

CONTEXT

Intraoperative hemodynamic instability (HDI) can lead to cardiovascular and cerebrovascular complications during surgery for pheochromocytoma/paraganglioma (PPGL).

OBJECTIVES

We aimed to assess the risk of intraoperative HDI in patients with PPGL to improve surgical outcome.

METHODS

A total of 199 consecutive patients with PPGL confirmed by surgical pathology were retrospectively included in this study. This cohort was separated into 2 groups according to intraoperative systolic blood pressure, the HDI group (n = 101) and the hemodynamic stability (HDS) group (n = 98). It was also divided into 2 subcohorts for predictive modeling: the training cohort (n = 140) and the validation cohort (n = 59). Prediction models were developed with both the ensemble machine learning method (EL model) and the multivariate logistic regression model using body composition parameters on computed tomography, tumor radiomics, and clinical data. The efficiency of the models was evaluated with discrimination, calibration, and decision curves.

RESULTS

The EL model showed good discrimination between the HDI group and HDS group, with an area under the curve of (AUC) of 96.2% (95% CI, 93.5%-99.0%) in the training cohort, and an AUC of 93.7% (95% CI, 88.0%-99.4%) in the validation cohort. The AUC values from the EL model were significantly higher than the logistic regression model, which had an AUC of 74.4% (95% CI, 66.1%-82.6%) in the training cohort and an AUC of 74.2% (95% CI, 61.1%-87.3%) in the validation cohort. Favorable calibration performance and clinical applicability of the EL model were observed.

CONCLUSION

The EL model combining preoperative computed tomography-based body composition, tumor radiomics, and clinical data could potentially help predict intraoperative HDI in patients with PPGL.

Breast Imaging Reporting and Data System evaluation of breast lesions improved with virtual touch tissue imaging average grayscale values

BACKGROUND

Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients' survival.

OBJECTIVE

We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy.

METHODS

We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS+VAGV.

RESULTS

VAGV was lower in malignant tumors than in benign ones (35.82 ± 13.39 versus 73.58 ± 42.69, P< 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS+VAGV, BI-RADS+VAGV had the highest AUC (0.926 versus 0.882, P= 0.0066; 0.926 versus 0.834, P= 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC= 0.9796) and substantial agreement using BI-RADS (Kappa= 0.725).

CONCLUSION

Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS.

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.

Feature selection based on unsupervised clustering evaluation for predicting neoadjuvant chemoradiation response for patients with locally advanced rectal cancer

Accurate response prediction allows for personalized cancer treatment of locally advanced rectal cancer (LARC) with neoadjuvant chemoradiation. In this work, we designed a convolutional neural network (CNN) feature extractor with switchable 3D and 2D convolutional kernels to extract deep learning features for response prediction. Compared with radiomics features, convolutional kernels may adaptively extract local or global image features from multi-modal MR sequences without the need of feature predefinition. We then developed an unsupervised clustering based evaluation method to improve the feature selection operation in the feature space formed by the combination of CNN features and radiomics features. While normal process of feature selection generally includes the operations of classifier training and classification execution, the process needs to be repeated many times after new feature combinations were found to evaluate the model performance, which incurs a significant time cost. To address this issue, we proposed a cost effective process to use a constructed unsupervised clustering analysis indicator to replace the classifier training process by indirectly evaluating the quality of new found feature combinations in feature selection process. We evaluated the proposed method using 43 LARC patients underwent neoadjuvant chemoradiation. Our prediction model achieved accuracy, area-under-curve (AUC), sensitivity and specificity of 0.852, 0.871, 0.868, and 0.735 respectively. Compared with traditional radiomics methods, the prediction models (AUC = 0.846) based on deep learning-based feature sets are significantly better than traditional radiomics methods (AUC = 0.714). The experiments also showed following findings: (1) the features with higher predictive power are mainly from high-order abstract features extracted by CNN on ADC images and T2 images; (2) both ADC_Radiomics and ADC_CNN features are more advantageous for predicting treatment responses than the radiomics and CNN features extracted from T2 images; (3) 3D CNN features are more effective than 2D CNN features in the treatment response prediction. The proposed unsupervised clustering indicator is feasible with low computational cost, which facilitates the discovery of valuable solutions by highlighting the correlation and complementarity between different types of features.

Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer

OBJECTIVES

To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset.

METHODS

Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97).

RESULTS

After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables.

CONCLUSIONS

Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset).

CLINICAL RELEVANCE STATEMENT

Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization.

KEY POINTS

This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.

Radiomics for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Prospective Observational Trial

(1) Background: An increasing amount of research has supported the role of radiomics for predicting pathological complete response (pCR) to neoadjuvant chemoradiation treatment (nCRT) in order to provide better management of locally advanced rectal cancer (LARC) patients. However, the lack of validation from prospective trials has hindered the clinical adoption of such studies. The purpose of this study is to validate a radiomics model for pCR assessment in a prospective trial to provide informative insight into radiomics validation. (2) Methods: This study involved a retrospective cohort of 147 consecutive patients for the development/validation of a radiomics model, and a prospective cohort of 77 patients from two institutions to test its generalization. The model was constructed using T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI to understand the associations with pCR. The consistency of physicians' evaluations and agreement on pathological complete response prediction were also evaluated, with and without the aid of the radiomics model. (3) Results: The radiomics model outperformed both physicians' visual assessments in the prospective test cohort, with an area under the curve (AUC) of 0.84 (95% confidence interval of 0.70-0.94). With the aid of the radiomics model, a junior physician could achieve comparable performance as a senior oncologist. (4) Conclusion: We have built and validated a radiomics model with pretreatment MRI for pCR prediction of LARC patients undergoing nCRT.

External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study

OBJECTIVES

The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers' discriminative performance for pCR prediction.

METHODS

This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve.

RESULTS

Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models.

CONCLUSIONS

The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients.

KEY POINTS

• A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. • The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. • The RF classifier performed best in the current study.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Objectives

To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).

Methods

Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC).

Results

There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model.

Conclusion

MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models

BACKGROUND

Radiomics is an approach to medical imaging that quantifies the features normally translated into visual display. While both radiomic and clinical markers have shown promise in predicting response to neoadjuvant chemoradiation therapy (nCRT) for rectal cancer, the interrelationship is not yet clear.

METHODS

A retrospective, single-institution study of patients treated with nCRT for locally advanced rectal cancer was performed. Clinical and radiomic features were extracted from electronic medical record and pre-treatment magnetic resonance imaging, respectively. Machine learning models were created and assessed for complete response and positive treatment effect using the area under the receiver operating curves.

RESULTS

Of 131 rectal cancer patients evaluated, 68 (51.9%) were identified to have a positive treatment effect and 35 (26.7%) had a complete response. On univariate analysis, clinical T-stage (OR 0.46, p = 0.02), lymphovascular/perineural invasion (OR 0.11, p = 0.03), and statin use (OR 2.45, p = 0.049) were associated with a complete response. Clinical T-stage (OR 0.37, p = 0.01), lymphovascular/perineural invasion (OR 0.16, p = 0.001), and abnormal carcinoembryonic antigen level (OR 0.28, p = 0.002) were significantly associated with a positive treatment effect. The clinical model was the strongest individual predictor of both positive treatment effect (AUC = 0.64) and complete response (AUC = 0.69). The predictive ability of a positive treatment effect increased by adding tumor and mesorectal radiomic features to the clinical model (AUC = 0.73).

CONCLUSIONS

The use of a combined model with both clinical and radiomic features resulted in the strongest predictive capability. With the eventual goal of tailoring treatment to the individual, both clinical and radiologic markers offer insight into identifying patients likely to respond favorably to nCRT.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer

In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.

Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis

Purpose

The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models.

Methods

We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I² values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity.

Results

We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score.

Conclusions

Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.

Combination of Changes in CEA and CA199 Concentration After Neoadjuvant Chemoradiotherapy Could Predict the Prognosis of Stage II/III Rectal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Followed by Total Mesorectal Excision

Background

Previous studies have shown that the levels of serum tumor markers CEA and CA19-9 were related to chemoradiotherapy. Therefore, it has been assumed that dynamic monitoring of these markers could predict the prognosis of stage II/III rectal cancer (RC). Therefore, this study proposed to evaluate the prognostic value of changes in serum tumor biomarkers for stage II/III RC patients undergoing neoadjuvant chemoradiotherapy (NCRT) followed by total mesorectal excision (TME).

Methods

A total of 217 patients with stage II/III RC receiving NCRT followed by TME were retrospectively analyzed. Serum CEA and CA199 levels were measured within one week before NCRT and one week before TME. The optimal cut-off points of ∆CEA% and ∆CA199% for prognosis prediction were calculated by receiver operating characteristics (ROC) analysis. Independent prognostic predictors were identified by univariate and multivariate Cox regression analyses. To avoid the efficiency of ∆CEA% and ∆CA199% on serum tumor biomarker change (STBC) score, two models including and excluding ∆CEA% and ∆CA199% were established separately in multivariate analysis.

Results

The optimal cut-off point for ∆CEA% and ∆CA199% were −30.29% and 20.30%, respectively. Univariate analysis showed that ∆CEA%, ∆CA199%, STBC score, ypT staging and yN staging could predict OS. ypT staging and STBC score could predict DFS. In multivariate analysis, only ∆CA199% (HR = 0.468, 95% CI: 0.220–0.994, p = 0.048), ypT staging (HR = 0.420, 95% CI: 0.182–0.970, p = 0.042), and STBC score (HR = 0.204, 95% CI: 0.078–0.532, p = 0.001) were independently related to OS; and STBC score (HR = 0.412, 95% CI: 0.216–0.785, p=0.007) and ypT staging (HR = 0.421, 95% CI: 0.224–0.792, p = 0.007) were independently related to DFS.

Conclusion

We established a combined STBC score to predict the prognosis of stage II/III RC patients receiving NCRT followed by TME. The predictive value of the combined score was stronger than a single marker alone and even stronger than several pathological indicators.

Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially ¹²⁵I CT-guided radioactive seed implant brachytherapy.

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer

BACKGROUND AND PURPOSE

Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.

MATERIALS AND METHODS

Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application.

RESULTS

This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance.

CONCLUSION

Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer

Objective

This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images.

Background

nCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty.

Methods

842 LARC patients treated with standard nCRT from three medical centers were retrospectively recruited and subgrouped into the training, testing and external validation sets. Treatment response was classified as pCR and non-pCR based on the pathological diagnosis after surgery as the ground truth. The hematoxylin & eosin (H&E)-stained biopsy slides were manually annotated and used to develop a deep pathological complete response (DeepPCR) prediction model by deep learning.

Results

The proposed DeepPCR model achieved an AUC-ROC of 0.710 (95% CI: 0.595, 0.808) in the testing cohort. Similarly, in the external validation cohort, the DeepPCR model achieved an AUC-ROC of 0.723 (95% CI: 0.591, 0.844). The sensitivity and specificity of the DeepPCR model were 72.6% and 46.9% in the testing set and 72.5% and 62.7% in the external validation cohort, respectively. Multivariate logistic regression analysis showed that the DeepPCR model was an independent predictive factor of nCRT (P=0.008 and P=0.004 for the testing set and external validation set, respectively).

Conclusions

The DeepPCR model showed high accuracy in predicting pCR and served as an independent predictive factor for pCR. The model can be used to assist in clinical treatment decision making before surgery.

Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models

Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.

Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study

Background and Purpose

Computerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT).

Materials and Methods

Patients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort.

Results

The pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone.

Conclusion

Our combined predictive model was robust in differentiating patients with and without response to nCRT.

Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer

Background

To report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum.

Methods

Consecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.

Results

Two-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).

Conclusions

Radiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02053-y.

Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort

BACKGROUND

Predicting pathological complete response (pCR) in patients affected by locally advanced rectal cancer (LARC) who undergo neoadjuvant chemoradiotherapy (nCRT) is a challenging field of investigation, but many of the published models are burdened by a lack of reliable external validation. Aim of this study was to evaluate the applicability of a magnetic resonance imaging (MRI) radiomic-based pCR model developed and validated in Europe, to a different cohort of patients from an intercontinental cancer center.

METHODS

The original model was based on two clinical and two radiomics features extracted from T2-weighted 1.5 T MRI of 161 LARC patients acquired before nCRT, considered as training set. Such model is here validated using the T2-w 1.5 and 3 T staging MRI of 59 LARC patients with different clinical characteristics consecutively treated in mainland Chinese cancer center from March 2017 to January 2018. Model performance were evaluated in terms of area under the receiver operator characteristics curve (AUC) and relative parameters, such as accuracy, specificity, negative and positive predictive value (NPV and PPV).

RESULTS

An AUC of 0.83 (CI 95%, 0.71-0.96) was achieved for the intercontinental cohort versus a value of 0.75 (CI 95%, 0.61-0.88) at the external validation step reported in the original experience. Considering the best cut-off threshold identified in the first experience (0.26), the following predictive performance were obtained: 0.65 as accuracy, 0.64 as specificity, 0.70 as sensitivity, 0.91 as NPV and 0.28 as PPV.

CONCLUSIONS

Despite the introduction of significant different factors, the proposed model appeared to be replicable on a real-world data extra-European patients' cohort, achieving a TRIPOD 4 level.

MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer

Background

To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC).

Methods

Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation.

Results

We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance.

Conclusions

The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.

Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

PURPOSE

This study aims to further enhance a validated radiomics-based model for predicting pathologic complete response (pCR) after chemo‑radiotherapy in locally advanced rectal cancer (LARC) for use in clinical practice.

METHODS

A generalized linear model (GLM) to predict pCR in LARC patients previously trained in Europe and validated with an external inter-continental cohort (59 patients), was first examined with further 88 intercontinental patient datasets to assess its reproducibility; then new radiomics and clinical features, and validation methods were investigated to build a new model for enhancing the pCR prediction for patients admitted to our department. The patients were divided into training group (75%) and validation group (25%) according to their demographic. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to reduce the dimensionality of the extracted features of the training group and select the optimal ones; the performance of the reference GLM and enhanced models was compared through the area under curve (AUC) of the receiver operating characteristics.

RESULTS

The value of AUC of the reference model was 0.831 (95% CI, 0.701-0.961), and 0.828 (95% CI, 0.700-0.956) in the original and new validation cohorts, respectively, showing a reproducibility in the applicability of the GLM model. Eight features were found to be significant with LASSO and used to establish an enhanced model. The AUC of the enhanced model of 0.926 (95% CI, 0.859-0.993) for training, and 0.926 (95% CI, 0.767-1.00) for the validation group shows better performance than the reference model.

CONCLUSIONS

The GLM model shows good reproducibility in predicting pCR in LARC; the enhanced model has the potential to improve prediction accuracy and may be a candidate in clinical practice.

Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics

BACKGROUND

Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness.

PURPOSE

To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT.

STUDY TYPE

Retrospective.

POPULATION

A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC.

FIELD STRENGTH/SEQUENCE

A 3.0-T; fast spin echo T₂ -weighted and single-shot EPI diffusion-weighted images.

ASSESSMENT

The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model.

STATISTICAL TESTS

The χ² test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference.

RESULTS

Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups.

DATA CONCLUSION

A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 4.

Preoperative T and N Restaging of Rectal Cancer After Neoadjuvant Chemoradiotherapy: An Accuracy Comparison Between MSCT and MRI

Background

It is well established that magnetic resonance imaging (MRI) is better than multi-slice computed tomography (MSCT) for the accurate diagnosis of pretreatment tumor (T) and node (N) staging of rectal cancer. However, the diagnostic value of MRI and MSCT in local restaging of rectal cancer after neoadjuvant chemoradiotherapy (NCRT) is controversial. The aim of this study is to investigate the performance of the two imaging exams in restaging of patients with rectal cancer.

Methods

Patients with rectal cancer from April 2015 to April 2021 were analyzed retrospectively. The inclusion criteria are as follows: 1) diagnosis of rectal cancer through pathology; 2) NCRT had been performed; 3) all patients had undergone both MSCT and MRI examination before the surgery. Exclusion criteria are as follows: 1) incomplete clinical and imaging data; 2) previous history of pelvic surgery. Two radiologists performed T and N staging of patient images. Diagnostic accuracy, consistency analysis, and error restaging distribution of the two imaging exams for T and N restaging of rectal cancer were assessed using postoperative pathological staging as the gold standard.

Results

A total of 62 patients (49 men; mean age: 59 years; age range 29–83 years) were included in the study. The diagnostic accuracy of MSCT and MRI for T restaging was 51.6% (95% CI 39.3%–63.9%) and 41.9% (95% CI 29.6%–54.2%), respectively, and no statistical difference was found between them (p > 0.05). The diagnostic accuracy of MSCT and MRI for N restaging was 56.5% (95% CI 44.2%–68.8%) and 53.2% (95% CI 40.8%–65.6%), respectively, and no statistical difference was found between them (p > 0.05). The consistency analysis showed that T restaging (κ = 0.583, p < 0.001) and N restaging (κ = 0.644, p < 0.001) were similar between MSCT and MRI. There was no significant difference in the distribution of over, accurate, or low staging in T restaging (p > 0.05) and N restaging (p > 0.05) between MSCT and MRI.

Conclusions

MSCT and MRI have similarly poor performance in the diagnosis of preoperative T and N restaging of rectal cancer after NCRT. Neither of them cannot effectively stage the ypT0-1 of rectal cancer. These findings may be of clinical relevance for planning less imaging exam.

Rectal MRI radiomics for predicting pathological complete response: Where we are

Radiomics using rectal MRI radiomics has emerged as a promising approach in predicting pathological complete response. In this study, we present a typical pipeline of a radiomics analysis and review recent studies, exploring applications, development of radiomics methodologies and model construction in pCR prediction. Finally, we will offer our opinion about the future and discuss the next steps of rectal MRI radiomics for predicting pCR.

Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

BACKGROUND

Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided.

METHODS

Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models.

RESULTS

A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration.

CONCLUSIONS

Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.

Role of MRI‑based radiomics in locally advanced rectal cancer (Review)

Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision‑making. Radiomics is characterized by the extraction of high‑dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI‑based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision‑making. The present review summarizes the applications, limitations and prospects of MRI‑based radiomics in LARC.

The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI

Simple Summary

Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters.

Abstract

Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.

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.

Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrated system. Although this combination is efficient in some tasks, the deep learning-based classification network is often difficult to capture an effective representation of lesion regions, and prone to face the challenge of overfitting, leading to unreliable features and inaccurate results, especially when the sizes of the lesions are small or the training dataset is small. In addition, these combinations mostly lack an effective feature selection mechanism, which makes it difficult to obtain the optimal feature selection. In this paper, we introduce a novel and effective deep semantic segmentation feature-based radiomics (DSFR) framework to overcome the above-mentioned challenges, which consists of two modules: the deep semantic feature extraction module and the feature selection module. Specifically, the extraction module is utilized to extract hierarchical semantic features of the lesions from a trained segmentation network. The feature selection module aims to select the most representative features by using a novel feature similarity adaptation algorithm. Experiments are extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs), and the prediction of thrombolytic therapy efficacy in deep venous thrombosis (DVT). Experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.

Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer

OBJECTIVES

Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC.

METHODS

A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves.

RESULTS

The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value.

CONCLUSIONS

We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC.

KEY POINTS

• There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.

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.

Early conformational changes at tumour bed and long term response after neoadjuvant therapy in locally-advanced rectal cancer

OBJECTIVES

To evaluate how changes in tumour scar depth angle and thickness in the post-neoadjuvant period relate to long-term response in locally-advanced rectal cancer patients.

METHODS

Informed consent was obtained from all patients and institutional review board approved this retrospective study. Sixty-nine consecutive locally-advanced rectal cancer patients who underwent neoadjuvant therapy and were selected for "Watch-and-Wait" were enrolled. Two radiologists, O1 and O2, blindly and independently reviewed the 1st and 2nd post-neoadjuvant therapy pelvic MRI T2-weighted images and recorded depth angle and thickness of the tumour scar. Value changes were calculated by simple subtraction (2nd-1st). Mann-Whitney U test was employed to assess for significant differences between sustained clinical complete responders (SCR), defined as patients with pathologic complete response or clinical complete response with a minimum follow-up of 1 year; and non-sustained complete responders (non-SCR). Interobserver agreement was estimated using intraclass correlation coefficient (ICC). Data on mrTRG, DWI and endoscopy at 1st and 2nd timepoints were retrieved for comparison.

RESULTS

In SCR, depth angle change between 1st (med = 10 weeks after end of radiotherapy) and 2nd (med = 23 weeks after end of radiotherapy) timepoints was significantly different (O1:p = 0.004; O2:p = 0.010): the SCR group showed a depth angle reduction (O1:med=-4.45; O2:med=-2.35), whereas non-SCRs showed a depth angle increase (O1:med=+2.60; O2:med=+7.40). Also, at 2nd timepoint, SCR scars were significantly thinner both for O1 (p = 0.003; SCR:med = 7.05 mm; non-SCR:med = 9.4 mm) and O2 (p = 0.006; SCR:med = 6.45 mm; non-SCR:med = 8.2 mm). A depth angle increase >21^º between 1st and 2nd timepoints and a scar thickness >10 mm at 2nd timepoint were not sensitive but were highly specific for a non-SCR (91/94 %) for both observers. Interobserver agreement was good for scar depth angle change (ICC = 0.65) and excellent for scar thickness at 2nd timepoint (ICC = 0.84). Of the retrieved data, only DWI at 2nd timepoint was discriminative (p = 0.043) providing a similar sensitivity (33 %) and a slightly lower specificity (87.5 %).

CONCLUSION

Tumour scar expansion >21° between 1st and 2nd post-neoadjuvancy MRI and a scar thickness >10 mm at 2nd post-neoadjuvancy MRI may consistently indicate a non-SCR with high specificity in locally-advanced rectal cancer patients.

Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging

INTRODUCTION

To develop an image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance (MR) imaging.

MATERIALS AND METHODS

A total of 466 patients with locally advanced rectal cancer who received preoperative chemoradiotherapy followed by surgical resection were collected from single center, among whom 113 (24.3%) were allocated to the holdout testing set. Complete response (pCR) was defined as Dworak tumor regression grade (TRG) 4, while good response (GR) was defined as TRG 3 or 4. Based on post-chemoradiotherapy T2-weighted axial MR images, two deep learning models were developed to predict pCR and GR, respectively. The prediction performance of the deep learning models was evaluated in the testing set and was compared to that of a senior radiologist and a radiation oncologist.

RESULTS

The deep learning model showed an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 0.76, 0.30, 0.96, 0.67, 0.87, and 85.0% for predicting pCR and 0.72, 0.54, 0.81, 0.60, 0.77, and 71.7% for predicting GR, respectively. The deep learning model had a superior predictive performance than the observers. Fair agreement between the ground truth and the model was shown for pCR prediction (kappa = 0.34) and GR prediction (kappa = 0.36).

CONCLUSIONS

The post-chemoradiotherapy T2-weighted axial MR image-based deep learning model showed acceptable performance in predicting pCR or GR in patients with rectal cancer, compared with human observers.

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

Background

We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature.

Methods

This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score.

Results

We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did.

Conclusions

The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02919-x.

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges

The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.

Can the mesorectal fat tissue volume be used as a predictive factor in foreseeing the response to neoadjuvant chemoradiotherapy in rectum cancer? A CT-based preliminary study

PURPOSE

This study was to investigate the effect of mesorectal fat tissue volume (MRV) on the pathological response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer.

METHODS

88 patients who had been diagnosed with locally advanced rectal cancer between January 2017 and June 2020 were reviewed retrospectively. The total abdominal, subcutaneous, visceral, and mesorectal fatty tissue components were measured semiquantitatively by two radiologists using computed tomography (CT)-based findings. The patients were divided into two groups as those with and without a pathological response to nCRT. The relationship of MRV with the other fat tissue components of the body was also evaluated.

RESULTS

We performed a retrospective analysis of 88 patients (mean age 62.7 years [range, 33-90 years]; 31 males and 57 females). A positive response to nCRT was present in 47 patients. There were 59 patients with stage 3 disease. 46 patients demonstrated lymph node involvement. The mean MRV was 69.6 ± 31.0 ml in no-response group and 105.8 ± 47.5 ml in response-positive patients (p < 0.05). MRV showed the highest correlation with visceral fat volume (VFV). There was a negative correlation between the MRV and the N stage. A cut-off value of ≥ 69.4 for MRV predicted the repsonse to nCRT, with 82.9% sensitivity and 58.5% specificity [AUC: 0.757 (0.653-0.842), p < 0.001] in receiver operating characteristic (ROC) curve analysis CONCLUSIONS: MRV can be used as a novel parameter in predicting of pathological response to nCRT in locally advanced rectal cancer patients.

Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

Development of a Joint Prediction Model Based on Both the Radiomics and Clinical Factors for Predicting the Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

Purpose

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for locally advanced rectal cancer (LARC). However, the accuracy of traditional clinical indicators in predicting tumor response is poor. Recently, radiomics based on magnetic resonance imaging (MRI) has been regarded as a promising noninvasive assessment method. The present study was conducted to develop a model to predict the pathological response by analyzing the quantitative features of MRI and clinical risk factors, which might predict the therapeutic effects in patients with LARC as accurately as possible before treatment.

Patients and Methods

A total of 82 patients with LARC were enrolled as the training cohort and internal validation cohort. The pre-CRT MRI after pretreatment was acquired to extract texture features, which was finally selected through the minimum redundancy maximum relevance (mRMR) algorithm. A support vector machine (SVM) was used as a classifier to classify different tumor responses. A joint radiomics model combined with clinical risk factors was then developed and evaluated by receiver operating characteristic (ROC) curves. External validation was performed with 107 patients from another center to evaluate the applicability of the model.

Results

Twenty top image texture features were extracted from 6192 extracted-radiomic features. The radiomics model based on high-spatial-resolution T2-weighted imaging (HR-T2WI) and contrast-enhanced T1-weighted imaging (T1+C) demonstrated an area under the curve (AUC) of 0.8910 (0.8114–0.9706) and 0.8938 (0.8084–0.9792), respectively. The AUC value rose to 0.9371 (0.8751–0.9997) and 0.9113 (0.8449–0.9776), respectively, when the circumferential resection margin (CRM) and carbohydrate antigen 19-9 (CA19-9) levels were incorporated. Clinical usefulness was confirmed in an external validation cohort as well (AUC, 0.6413 and 0.6818).

Conclusion

Our study indicated that the joint radiomics prediction model combined with clinical risk factors showed good predictive ability regarding the treatment response of tumors as accurately as possible before treatment.

Magnetic Resonance Imaging-Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two-Center Study

BACKGROUND

Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed.

PURPOSE

To develop and test an magnetic resonance imaging (MRI)-based radiomics nomogram for predicting the grade of STS (low-grade vs. high grade).

STUDY TYPE

Retrospective POPULATION: One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71).

FIELD STRENGTH/SEQUENCE

Unenhanced T1-weighted (T1WI) and fat-suppressed T2-weighted images (FS-T2WI) were acquired at 1.5 T and 3.0 T.

ASSESSMENT

Clinical-MRI characteristics included age, gender, tumor-node-metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression-free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS-T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS-T1， RS-FST2, and RS-Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors.

STATISTICAL TESTS

Clinical-MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS-T1 model, RS-FST2 model, and RS-Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS-Combined model had AUCs of 0.916 (95%CI, 0.866-0.966, training set) and 0.879 (95%CI, 0.791-0.967, external validation set), and demonstrated good calibration and good clinical utility.

DATA CONCLUSION

The proposed noninvasive MRI-based radiomics models showed good performance in differentiating low-grade from high-grade STSs.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Accurate nomograms with excellent clinical value for locally advanced rectal cancer

Background

Rectal cancer accounts for approximately 30–50% of colorectal cancer. Despite its widespread use and convenience, the American Joint Committee on Cancer (AJCC) staging system for predicting survival is prone to inaccuracy, even including a survival paradox for locally advanced rectal cancer (LARC). An accurate risk stratification of LARC is essential for proper treatment selection and prognostic evaluation. Therefore, we aimed to create prognostic nomograms for LARC capable of assessing overall survival (OS) and cancer-specific survival (CSS) precisely and intuitively.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was accessed. All of the significant variables in the multivariate analysis were integrated to build the nomograms.

Results

Data for a total of 23,055 patients with LARC were collected from the SEER database in this study. Based on the multivariate Cox regression analysis, both OS and CSS were significantly associated with 13 variables: age, marital status, race, pathological grade, histological type, T stage, N stage, surgery, radiotherapy, chemotherapy, regional nodes examined (RNE), tumor size, and carcinoembryonic antigen (CEA). These were included in the construction of nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA), concordance index, and calibration curves demonstrated the discriminative superiority of the nomograms.

Conclusions

The nomograms, which effectively solve the issue of the survival paradox in the AJCC staging system regarding LARC, may act as excellent tools for integrating clinical characteristics and to guiding therapeutic choices for LARC patients.

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.

Methods

Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.

Results

A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.

Conclusion

We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.