MRI-Based Radiomic Models Outperform Radiologists in Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

MRI radiomics combined with delta-radiomics model for predicting pathological complete response in locally advanced rectal cancer patients after neoadjuvant chemoradiotherapy: A multi-institutional study

PURPOSE

To construct and validate a magnetic resonance imaging (MRI) radiomics combined with delta-radiomics and clinical information (C) model for predicting pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT).

METHODS

A total of 198 patients with LARC who underwent MRI before and after nCRT were retrospectively enrolled in this multi-institutional retrospective study. MRI radiomics features were extracted from pre- and post-nCRT diffusion weighted imaging (DWI) and T2-weighted imaging (T2WI) images. The least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) algorithm were used to select the optimal predictive features. We constructed the following models, four single-modal radiomics models: DWI-post, DWI-pre, T2-post, T2-pre, two delta-radiomics models: DWI-delta, T2-delta and four multi-modal fusion models: DWI-post + DWI-pre, DWI-post + DWI-delta, DWI-post + DWI-delta + T2-delta, DWI-post + DWI-delta + T2-delta + C. The models were developed using four machine learning classifiers, including Decision Tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost).

RESULTS

The multi-modal fusion model DWI-post + DWI-delta + T2-delta achieved the best performance with an area under the curve (AUC) of 0.879 for predicting pCR, which was significantly higher than that of the single-modal model DWI-post (optimal AUC = 0.824), DWI-pre (optimal AUC = 0.836) and the delta-radiomics model DWI-delta (optimal AUC = 0.841), T2-delta (optimal AUC = 0.837) in the internal validation sets. XGBoost classifier showed better prediction performance than the other classifiers in the most models. The DWI-post + DWI-pre model with DT classifier and PCA feature selection achieved the highest AUC of 0.754 and the DWI-post + DWI-delta + T2-delta + C model with SVM classifier and LASSO feature selection achieved the suboptimal AUC of 0.734 in the external validation sets.

CONCLUSION

The multi-modal fusion model significantly outperforms conventional single-modal prediction models. The model could be used as a reliable and noninvasive tool for the personalized therapy in LARC patients.

MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer

PURPOSE

Individual prognosis assessment is of paramount importance for treatment decision-making and active surveillance in cancer patients. We aimed to propose a radiomic model based on pre- and post-therapy MRI features for predicting disease-free survival (DFS) in locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) and subsequent surgical resection.

METHODS

This retrospective study included a total of 126 LARC patients, which were randomly assigned to a training set (n = 84) and a validation set (n = 42). All patients underwent pre- and post-nCRT MRI scans. Radiomic features were extracted from higher resolution T2-weighted images. Pearson correlation analysis and ANOVA or Relief were utilized for identifying radiomic features associated with DFS. Pre-treatment, post-treatment, and delta radscores were constructed by machine learning algorithms. An individualized nomogram was developed based on significant radscores and clinical variables using multivariate Cox regression analysis. Predictive performance was evaluated by the C-index, calibration curve, and decision curve analysis.

RESULTS

The results demonstrated that in the validation set, the clinical model including pre-surgery carcinoembryonic antigen (CEA), chemotherapy after radiotherapy, and pathological stage yielded a C-index of 0.755 (95% confidence interval [CI]: 0.739-0.771). While the optimal pre-, post-, and delta-radscores achieved C-indices of 0.724 (95%CI: 0.701-0.747), 0.701 (95%CI: 0.671-0.731), and 0.625 (95%CI: 0.589-0.661), respectively. The nomogram integrating pre-surgery CEA, pathological stage, alongside pre- and post-nCRT radscore, obtained the highest C-index of 0.833 (95%CI: 0.815-0.851). The calibration curve and decision curves exhibited good calibration and clinical usefulness of the nomogram. Furthermore, the nomogram categorized patients into high- and low-risk groups exhibiting distinct DFS (both P < 0.0001).

CONCLUSIONS

The nomogram incorporating pre- and post-therapy radscores and clinical factors could predict DFS in patients with LARC, which helps clinicians in optimizing decision-making and surveillance in real-world settings.

MRI radiomics prediction modelling for pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis

PURPOSE

Predicting response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is challenging. Organ preservation strategies can be offered to patients with complete clinical response. We aim to evaluate MRI-derived radiomics models in predicting complete pathological response (pCR).

METHODS

Search included MEDLINE, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR) for studies published before 1st February 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess quality of included study. The research protocol was registered in PROSPERO (CRD42024512865). We calculated pooled area under the receiver operating characteristic curve (AUC) using a random-effects model. To compare AUC between subgroups the Hanley & McNeil test was performed.

RESULTS

Forty-four eligible studies (12,714 patients) were identified for inclusion in the systematic review. We selected thirty-five studies including 10,543 patients for meta-analysis. The pooled AUC for MRI radiomics predicted pCR in LARC was 0.87 (95% CI 0.84-0.89). In the subgroup analysis 3 T MRI field intensity had higher pooled AUC 0.9 (95% CI 0.87-0.94) than 1.5 T pooled AUC 0.82 (95% CI 0.80-0.83) p < 0.001. Asian ethnicity had higher pooled AUC 0.9 (95% CI 0.87-0.93) than non-Asian pooled AUC 0.8 (95% CI 0.75-0.84) p < 0.001.

CONCLUSION

We have demonstrated that 3 T MRI field intensity provides a superior predictive performance. The role of ethnicity on radiomics features needs to be explored in future studies. Further research in the field of MRI radiomics is important as accurate prediction for pCR can lead to organ preservation strategy in LARC.

Construction and Value Analysis of a Prognostic Assessment Model Based on Radiomics and Genetic Data for Colorectal Cancer

Aims/Background Colorectal cancer (CRC) is one of the major global health problems, with high morbidity and mortality, underscoring the need for new diagnostic and prognostic tools. Therefore, this study aims to evaluate the significance of integrating radiomics with genetic data in CRC prognostic assessment and improve the accuracy of prognosis prediction. Methods This study included computed tomography (CT) images from 225 CRC patients and RNA-seq information from 654 patients, obtained from the TICA database. Key radiomics features and genes were identified through radiomics feature extraction, least absolute shrinkage and selection operator (LASSO) regression analysis, and Kaplan-Meier survival analysis. Furthermore, a CRC prognostic model was constructed using these key genes and radiomics features. Results This study identified 170 key radiomics features. Out of them, five were significantly associated with CRC prognosis. Transcriptome data analysis identified 8 key genes, among which the high expressions of Inhibin Subunit Beta B (INHBB), Potassium Voltage-Gated Channel Subfamily Q Member 2 (KCNQ2), and Ubiquilin Like (UBQLNL) were significantly correlated with poor prognosis. Age, tumor stage, pathological T stage, and pathological N stage were determined as independent prognostic factors. Moreover, immune infiltration analysis demonstrated that the immune score of the low-risk group was higher than that of the high-risk group, with significant differences in some immune cells, and key genes were correlated with immune cells. Additionally, the constructed CRC prognostic model incorporating three genes, INHBB, KCNQ2, and UBQLNL, exhibited high prediction accuracy in the validation set, with area under the curve (AUC) values of 0.80, 0.87, and 0.84 at 1-year, 3-year, and 5-year, respectively, indicating good prediction performance and reliability of the model. Conclusion The multimodal data combining radiomics features and gene expression data can improve the accuracy of CRC prognostic assessment, providing a valuable prognostic prediction tool for clinical practice and helping to guide the selection and optimization of treatment regimens.

MRI-based radiomics for predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis

Purpose

To evaluate the value of MRI-based radiomics for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC) through a systematic review and meta-analysis.

Methods

A systematic literature search was conducted in PubMed, Embase, Proquest, Cochrane Library, and Web of Science databases, covering studies up to July 1st, 2024, on the diagnostic accuracy of MRI radiomics for predicting pCR in LARC patients following NCRT. Two researchers independently evaluated and selected studies using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and the Radiomics Quality Score (RQS) tool. A random-effects model was employed to calculate the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) for MRI radiomics in predicting pCR. Meta-regression and subgroup analyses were performed to explore potential sources of heterogeneity. Statistical analyses were performed using RevMan 5.4, Stata 17.0, and Meta-Disc 1.4.

Results

A total of 35 studies involving 9,696 LARC patients were included in this meta-analysis. The average RQS score of the included studies was 13.91 (range 9.00-24.00), accounting for 38.64% of the total score. According to QUADAS-2, there were risks of bias in patient selection and flow and timing domain, though the overall quality of the studies was acceptable. MRI-based radiomics showed no significant threshold effect in predicting pCR (Spearman correlation coefficient=0.119, P=0.498) but exhibited high heterogeneity (I2≥50%). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and DOR were 0.83, 0.82, 5.1, 0.23 and 27.22 respectively, with an area under the summary receiver operating characteristic (sROC) curve of 0.91. According to joint model analysis, publication year, country, multi-magnetic field strength, multi-MRI sequence, ROI structure, contour consistency, feature extraction software, and feature quantity after feature dimensionality reduction were potential sources of heterogeneity. Deeks' funnel plot suggested no significant publication bias (P=0.69).

Conclusions

MRI-based radiomics demonstrates high efficacy for predicting pCR in LARC patients following NCRT, holding significant promise for informing clinical decision-making processes and advancing individualized treatment in rectal cancer patients.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024611733.

Selection of rectal cancer patients for organ preservation after neoadjuvant therapy: value of T2W-MRI signal intensity

BackgroundOrgan preservation strategies have been widely implemented for rectal cancer (RC) patients with a good response after neoadjuvant chemoradiation (nCRT). However, to accurately select eligible patients remains one of the key diagnostic challenges.PurposeTo identify eligible candidates for organ preservation after nCRT in RC, by identifying luminal response and lymph node metastases, based on T2W-MRI signal intensities.Material and MethodsA total of 171 RC patients underwent MRI before and after nCRT. The primary tumor (pre-nCRT-MRI) and tumor remnant (post-nCRT-MRI) were manually delineated. Ten signal intensity features were extracted and delta features were calculated by subtraction. Histopathological evaluation classified patients as lymph node negative (ypN0) or positive (ypN+), and as good responders (GR) or partial/poor responders (PR). Five models were constructed based on the timing of imaging.Results42/170 (25%) patients had ypN+, and 72/152 (47%) patients were considered GR. Univariate analysis showed 13/40 signal intensity features were significantly different between luminal response groups and 4/40 between nodal response groups. In multivariate analysis, the Baseline + Restaging-model yielded the best results for both luminal and nodal response with AUCs in the test set of 0.81 (95% CI=0.67-0.95) and 0.74 (95% CI=0.59-0.90), respectively. To identify PR, the Delta-model yielded an AUC of 0.72 (95% CI=0.56-0.89) and the Delta + Restaging-model an AUC of 0.81 (95% CI=0.67-0.95), both were not able to differentiate nodal response. The models including solely baseline or restaging features were not predictive.ConclusionT2W-MRI signal intensities of the primary rectal tumor are related to the luminal and nodal response after nCRT and hold promise to identify patients eligible for organ preservation.

Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review

PURPOSE

This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the performance of MRI-based artificial intelligence (AI) models and explore factors affecting their diagnostic accuracy.

METHODS

The review followed PRISMA guidelines and is registered with PROSPERO (CRD42024628017). Literature searches were conducted in PubMed, Embase, and Cochrane Library using keywords such as "artificial intelligence," "rectal cancer," "MRI," and "pathological complete response." Articles involving deep learning models applied to MRI for predicting pCR were included, excluding non-MRI data and studies without AI applications. Data on study characteristics, MRI sequences, AI model details, and performance metrics were extracted. Quality assessment was performed using the PROBAST tool.

RESULTS

Out of 512 initial records, 26 studies met the inclusion criteria. Most studies demonstrated promising diagnostic performance, with AUC values for external validation typically exceeding 0.8. The use of T2W and diffusion-weighted imaging (DWI) MRI phases enhanced model accuracy compared to T2W alone. Larger datasets generally correlated with improved model performance. However, heterogeneity in model designs, MRI protocols, and the limited integration of clinical data were noted as challenges.

CONCLUSION

AI-enhanced MRI demonstrates significant potential in predicting pCR in rectal cancer, particularly with T2W + DWI sequences and larger datasets. While integrating clinical data remains controversial, standardizing methodologies and expanding datasets will further enhance model robustness and clinical utility.

Radiomics in rectal cancer: current status of use and advances in research

Rectal cancer is a leading cause of morbidity and mortality among patients with malignant tumors in China. In light of the advances made in therapeutic approaches such as neoadjuvant therapy and total mesorectal excision, precise preoperative assessment has become crucial for developing a personalized treatment plan. As an emerging technology, radiomics has gained widespread application in the diagnosis, assessment of treatment response, and analysis of prognosis for rectal cancer by extracting high-throughput quantitative features from medical images. Radiomics thus demonstrates considerable potential for optimizing clinical decision-making. In this paper, we reviewed recent research focusing on advances in the use of radiomics for managing rectal cancer. The review covers TNM staging of tumors, assessment of neoadjuvant therapy outcomes, and survival prediction. We also discuss the challenges and prospects for future developments in translational medicine, particularly the need for data standardization, consistent feature extraction methodologies, and rigorous model validation.

Integration of Deep Learning and Sub-regional Radiomics Improves the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients

RATIONALE AND OBJECTIVES

The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop and validate a model that integrates deep learning and sub-regional radiomics from MRI imaging to predict pathological complete response (pCR) in patients with LARC.

MATERIALS AND METHODS

We retrospectively enrolled 768 eligible participants from three independent hospitals who had received neoadjuvant chemoradiotherapy followed by radical surgery. Pretreatment pelvic MRI scans (T2-weighted), were collected for annotation and feature extraction. The K-means approach was used to segment the tumor into sub-regions. Radiomics and deep learning features were extracted by the Pyradiomics and 3D ResNet50, respectively. The predictive models were developed using the radiomics, sub-regional radiomics, and deep learning features with the machine learning algorithm in training cohort, and then validated in the external tests. The models' performance was assessed using various metrics, including the area under the curve (AUC), decision curve analysis, Kaplan-Meier survival analysis.

RESULTS

We constructed a combined model, named SRADL, which includes deep learning with sub-regional radiomics signatures, enabling precise prediction of pCR in LARC patients. SRADL had satisfactory performance for the prediction of pCR in the training cohort (AUC 0.925 [95% CI 0.894 to 0.948]), and in test 1 (AUC 0.915 [95% CI 0.869 to 0.949]) and in test 2 (AUC 0.902 [95% CI 0.846 to 0.945]). By employing optimal threshold of 0.486, the predicted pCR group had longer survival compared to predicted non-pCR group across three cohorts. SRADL also outperformed other single-modality prediction models.

CONCLUSION

The novel SRADL, which integrates deep learning with sub-regional signatures, showed high accuracy and robustness in predicting pCR to neoadjuvant chemoradiotherapy using pretreatment MRI images, making it a promising tool for the personalized management of LARC.

Combined transrectal ultrasound and radiomics model for evaluating the therapeutic effects of neoadjuvant chemoradiotherapy in locally advanced rectal cancer

PURPOSE

This study aimed to explore a combined transrectal ultrasound (TRUS) and radiomics model for predicting tumor regression grade (TRG) after neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC).

METHODS

Among 190 patients with LARC, 53 belonged to GRG and 137 to PRG. Eight TRUS parameters were identified as statistically significant (P < 0.05) for distinguishing between the groups, including PSVpre, LDpost, TDpost, CEUS-IGpost, LD change rate, TD change rate, RI change rate, and CEUS-IG downgrade. The accuracies of these individual parameters in predicting TRG were 0.42, 0.62, 0.56, 0.68, 0.67, 0.70, 0.63, and 0.71, respectively. The AUC values were 0.596, 0.597, 0.630, 0.752, 0.686, 0.660, 0.650, and 0.666, respectively. The multi-parameter ultrasonic logistic regression (MPU-LR) model achieved an accuracy of 0.816 and an AUC of 0.851 (95% CI: [0.792-0.909]). The optimal pre- and post-treatment radiomics models were RF (Mean-PCA-RFE-6) and AE (Zscore-PCA-RFE-12), with accuracies of 0.563 and 0.596 and AUCs of 0.601 (95% CI: [0.561-0.641]) and 0.662 (95% CI: [0.630-0.694]), respectively. The combined model (US-RADpre-RADpost) showed the highest predictive power with accuracy and AUC of 0.863 and 0.913.

CONCLUSIONS

The combined model based on TRUS and radiomics demonstrated remarkable predictive capability for TRG after NCRT. It serves as a precision tool for assessing NCRT response in patients with LARC, impacting treatment strategies.

Enhancing clinical complete response assessment in rectal cancer: integrating transanal multipoint full-layer puncture biopsy criteria: a systematic review

There is currently a lack of standardized criteria for evaluating clinical complete response (cCR) in rectal cancer post-neoadjuvant chemoradiotherapy (nCRT), often resulting in discrepancies with true pathological complete response (pCR). Staging local lesions via MRI is challenged by tissue edema and fibrosis post-nCRT, while endoscopic biopsy accuracy is compromised by residual cancer foci in the muscular layer. Transanal local excision offers a relatively accurate assessment of lesion regression but poses challenges including impaired anal function and elevated complication rates. Building on current diagnostic frameworks, we propose enhancing cCR assessment by integrating histological criteria from transanal multipoint full-layer puncture biopsy (TMFP). This approach aims to improve accuracy while minimizing complications, offering promise for patients opting for observation-based treatments. Further research is needed for definitive conclusions.

Multi-parametric MRI radiomics for predicting response to neoadjuvant therapy in patients with locally advanced rectal cancer

OBJECTIVE

This study aims to evaluate the application value of multi-parametric magnetic resonance imaging (MRI) radiomics in predicting the response of patients with locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy(nCRT), aiming to provide non-invasive biomarkers for clinical decision-making in personalized treatment.

METHODS

A retrospective analysis was conducted on the clinical data and imaging records of patients with LARC who received nCRT and total mesorectal excision (TME) in two medical centers from 2017 to 2023. The patients were divided into a training group and a test group in a 7:3 ratio. Through radiomics analysis, radiomics features of tumor volume and mesorectal fat at baseline, before and after neoadjuvant therapy were extracted. Radiomics models based on single sequences (T2WI, DWI) and multi-sequence fusion were constructed, and the logistic regression classifier model was used to evaluate the prediction performance.

RESULTS

A total of 82 patients were included, with 30 in the good response group and 52 in the poor response group. Through the selection of radiomics features, radiomics models based on baseline MRI of tumor volume, mesorectal fat, and differences before and after treatment (Delta) were constructed. The area under the receiver operating characteristic curve (AUC) of the multi-parametric radiomics fusion model in the training and test groups was 0.852 and 0.848, respectively, showing high prediction performance and good calibration.

CONCLUSION

This study demonstrates that the multi-parametric MRI radiomics model can effectively predict the response of patients with locally advanced rectal cancer to neoadjuvant chemoradiotherapy. Especially, the fusion model provides high accuracy and good calibration. This result is conducive to the formulation of personalized treatment plans and optimization of treatment strategies.

A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer

BACKGROUND

With rectum-sparing protocols becoming more common for rectal cancer treatment, this study aimed to predict the pathological complete response (pCR) to preoperative chemoradiotherapy (pCRT) in rectal cancer patients using pre-treatment MRI and a radiomics-based machine learning approach.

METHODS

We divided MRI-data from 102 patients into a training cohort (n = 72) and a validation cohort (n = 30). In the training cohort, 52 patients were classified as non-responders and 20 as pCR based on histological results from total mesorectal excision.

RESULTS

We trained various machine learning models using radiomic features to capture disease heterogeneity between responders and non-responders. The best-performing model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 73% and an accuracy of 70%, with a sensitivity of 78% and a positive predictive value (PPV) of 80%. In the validation cohort, the model showed a sensitivity of 81%, specificity of 75%, and accuracy of 80%.

CONCLUSIONS

These results highlight the potential of radiomics and machine learning in predicting treatment response and support the integration of advanced imaging and computational methods for personalized rectal cancer management.

Predicting pathological complete response following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer using merged model integrating MRI-based radiomics and deep learning data

BACKGROUND

To construct and compare merged models integrating clinical factors, MRI-based radiomics features and deep learning (DL) models for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

METHODS

Totally 197 patients with LARC administered surgical resection after nCRT were assigned to cohort 1 (training and test sets); meanwhile, 52 cases were assigned to cohort 2 as a validation set. Radscore and DL models were established for predicting pCR applying pre- and post-nCRT MRI data, respectively. Different merged models integrating clinical factors, Radscore and DL model were constituted. Their predictive performances were validated and compared by receiver operating characteristic (ROC) and decision curve analyses (DCA).

RESULTS

Merged models were established integrating selected clinical factors, Radscore and DL model for pCR prediction. The areas under the ROC curves (AUCs) of the pre-nCRT merged model were 0.834 (95% CI: 0.737-0.931) and 0.742 (95% CI: 0.650-0.834) in test and validation sets, respectively. The AUCs of the post-nCRT merged model were 0.746 (95% CI: 0.636-0.856) and 0.737 (95% CI: 0.646-0.828) in test and validation sets, respectively. DCA showed that the pretreatment algorithm could yield enhanced clinically benefit than the post-nCRT approach.

CONCLUSIONS

The pre-nCRT merged model including clinical factors, Radscore and DL model constitutes an effective non-invasive tool for pCR prediction in LARC.

Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features

Establishing predictive models for the pathological response and lymph node metastasis in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT) based on MRI radiomic features derived from the tumor and mesorectal compartment (MC). This study included 209 patients with LARC who underwent rectal MRI both before and after nCRT. The patients were divided into a training set (n = 146) and a test set (n = 63). Regions of interest (ROIs) for the tumor and MC were delineated on both pre- and post-nCRT MRI images. Radiomic features were extracted, and delta radiomic features were computed. The predictive endpoints were pathological complete response (pCR), pathological good response (pGR), and lymph node metastasis (LNM). Feature selection for various models involved sequentially removing features with a correlation coefficient > 0.9, and features with P-values ≥ 0.05 in univariate analysis, followed by LASSO regression on the remaining features. Logistic regression models were developed, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC). Among the 209 LARC patients, the number of patients achieving pCR, pGR, and LNM were 44, 118, and 40, respectively. The optimal model for predicting each endpoint is the combined model that incorporates pre- and delta-radiomics features for both the tumor and MC. These models exhibited superior performance with AUC values of 0.874 (for pCR), 0.801 (for pGR), and 0.826 (for LNM), outperforming the MRI tumor regression grade (mrTRG) which yielded AUC values of 0.800, 0.715, and 0.603, respectively. The results demonstrate the potential utility of the tumor and MC radiomics features, in predicting treatment efficacy among LARC patients undergoing nCRT.

Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?

OBJECTIVE(S)

The treatment response to neoadjuvant chemoradiation (nCRT) differs largely in individuals treated for rectal cancer. In this study, we investigated the role of radiomics to predict the pathological response in locally advanced rectal cancers at different treatment time points: (1) before the start of any treatment using baseline T2-weighted MRI (T2W-MR) and (2) at the start of radiation treatment using planning CT.

METHODS

Patients on nCRT followed by surgery between June 2017 to December 2019 were included in the study. Histopathological tumour response grading (TRG) was used for classification, and gross tumour volume was defined by the radiation oncologists. Following resampling, 100 and 103 pyradiomic features were extracted from T2W-MR and planning CT images, respectively. Synthetic minority oversampling technique (SMOTE) was used to address class imbalance. Four machine learning classifiers built clinical, radiomic, and merged models. Model performances were evaluated on a held-out test dataset following 3-fold cross-validation using area under the receiver operator characteristic curves (AUC) with bootstrap 95% confidence intervals.

RESULTS

One hundred and fifty patients were included; 58/150 with TRG 1 were classified as complete responders, and rest were incomplete responders (IR). Clinical models performed better (AUC = 0.68) compared to radiomics models (AUC = 0.62). Overall, the clinical + T2W-MR model showed best performance (AUC = 0.72) in predicting the pathological response prior to therapy. Clinical + Planning CT-merged models could only achieve the highest AUC of 0.66.

CONCLUSION

Merging clinical and baseline T2W-MR radiomics enhances predicting pathological response in rectal cancer. Validation in larger cohorts is warranted, especially for watch and wait strategies.

Delta radiomics: an updated systematic review

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.

METHODS

Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool.

RESULTS

Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.

CONCLUSIONS

Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis

OBJECTIVES

To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging.

METHODS

We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before March 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity.

RESULTS

A total of 1699 patients from 24 studies were included. For machine learning models in predicting pCR to nCRT, the meta-analysis calculated a pooled area under the curve (AUC) of 0.91 (95% CI, 0.88-0.93), pooled sensitivity of 0.83 (95% CI, 0.74-0.89), and pooled specificity of 0.86 (95% CI, 0.80-0.91). We investigated 6 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 6 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep-learning model was 0.93 and 0.89 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90 and 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.93, and was 0.83 in studies conducted in other countries.

CONCLUSIONS

This systematic study showed that machine learning has promising potential in predicting pCR to nCRT in patients with locally advanced rectal cancer. Compared to traditional machine learning models, although deep-learning-based studies are less predominant and more heterogeneous, they are able to obtain higher AUC.

ADVANCES IN KNOWLEDGE

Compared to traditional machine learning models, deep-learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine learning-based models may bring us closer towards precision medicine.

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis

OBJECTIVE

Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer.

METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374).

RESULTS

Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005).

CONCLUSIONS

Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

MRI-based Multiregional Radiomics for Pretreatment Prediction of Distant Metastasis After Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

To develop and validate a nomogram based on intratumoral and peritumoral radiomics signatures for pretreatment prediction of distant metastasis-free survival (DMFS) in patients after neoadjuvant chemoradiotherapy (NCRT) with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This retrospective study included 230 patients (161 training cohort; 69 validation cohort) with LARC who underwent NCRT and surgery. Radiomics features were extracted on T2-weighted images from gross tumor volume (GTV) and volumes of 4-mm, 6-mm, and 8-mm peritumoral regions (PTV4, PTV6, and PTV8). The least absolute shrinkage and selection operator (LASSO)-Cox analysis were used for features selection and models construction. The performance of each model in predicting DMFS was evaluated by the Concordance index (C-index) and time-independent receiver operating characteristic curve (ROC).

RESULTS

The PTV4 radiomics model demonstrated superior performance compared to the PTV6 and PTV8 radiomics models, with C-indexes of 0.750 and 0.703 in the training and validation cohorts, respectively. The nomogram was constructed by integrating the GTV radiomics signature, PTV4 radiomics signature, and relevant clinical characteristics, including CA19-9 level, clinical T stage, and clinical N stage. The nomogram achieved C-indexes of 0.831 and 0.748, with corresponding AUCs of 0.872 and 0.808 for 5-year DMFS in the training and validation cohorts, respectively. Kaplan-Meier analysis revealed that a cut-off value of 1.653 effectively stratified patients into high- and low-risk groups for DM (P < 0.001).

CONCLUSION

The intra-peritumoral radiomics nomogram is a favorable tool for clinicians to develop personalized systemic treatment and intensive follow-up strategies to improve patient prognosis.

Implication of MRI Risk Stratification System on the Survival Benefits of Adjuvant Chemotherapy After Neoadjuvant Chemoradiotherapy in Patients With Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

To investigate the implication of a Magnetic resonance imaging (MRI) risk stratification system on the selection of patients with locally advanced rectal cancer (LARC) who can benefit from adjuvant chemotherapy (ACT) after neoadjuvant chemoradiotherapy (NCRT).

MATERIALS AND METHODS

This retrospective study included 328 patients with LARC who underwent NCRT and surgery. The median follow-up duration was 79 months (Interquartile range, 66-94 months). Cox logistic regression analysis was used to identify MRI risk factors and develop a risk stratification system to stratify patients into groups with high and low risks. Kaplan-Meier curves of distant metastasis-free survival (DMFS) and overall survival (OS) were used to show the benefits of ACT and stratify results based on the MRI risk stratification system and postoperative pathological staging.

RESULTS

An MRI risk stratification system was built based on four MRI risk factors, including MRI-identified T3b-T4 stage, N1-N2 stage, extramural venous invasion, and tumor deposits. 74 (22.6%) patients with 3-4 MRI risk factors were classified into the MRI high-risk group. ACT could significantly improve 5-year DMFS (19.2% versus 52.1%; p < 0.001) and OS (34.6% versus 75.0%; p < 0.001) for patients in the MRI high-risk group, while ACT had no survival benefit for patients in the MRI low-risk group. The benefits of ACT were not observed in patients with any pathological staging subgroups (ypT0-2N0, ypT3-4N0, and ypN+).

CONCLUSION

Patients in the MRI high-risk group could benefit from ACT, regardless of postoperative pathological staging. Baseline MRI should be considered more in ACT decision-making.