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

The Quality and Accuracy of Radiomics Model in Diagnosing Osteoporosis: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

The purpose of this study is to conduct a meta-analysis to evaluate the diagnostic performance of current radiomics models for diagnosing osteoporosis, as well as to assess the methodology and reporting quality of these radiomics studies.

METHODS

According to PRISMA guidelines, four databases including MEDLINE, Web of Science, Embase and the Cochrane Library were searched systematically to select relevant studies published before July 18, 2024. The articles that used radiomics models for diagnosing osteoporosis were considered eligible. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS) were used to assess the quality of included studies. The pooled diagnostic odds ratio (DOR), sensitivity, specificity, area under the summary receiver operator characteristic curve (AUC) were calculated to estimated diagnostic efficiency of pooled model.

RESULTS

A total of 25 studies were included, of which 24 provided usable data that were utilized for the meta-analysis, including 1553 patients with osteoporosis and 2200 patients without osteoporosis. The mean RQS score of included studies was 11.48 ± 4.92, with an adherence rate of 31.89%. The pooled DOR, sensitivity and specificity for model to diagnose osteoporosis were 81.72 (95% CI: 51.08 - 130.73), 0.90 (95% CI: 0.87-0.93) and 0.90 (95% CI: 0.87-0.93), respectively. The AUC was 0.96, indicating a high diagnostic capability. Subgroup analysis revealed that the use of different imaging modalities to construct radiomics models might be one source of heterogeneity. Radiomics models built using CT images and deep learning algorithms demonstrated higher diagnostic accuracy for osteoporosis.

CONCLUSION

Radiomics models for the diagnosis of osteoporosis have high diagnostic efficacy. In the future, radiomics models for diagnosing osteoporosis will be an efficient instrument to assist clinical doctors in screening osteoporosis patients. However, relevant guidelines should be followed strictly to improve the quality of radiomics studies.

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.

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.

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.

Deformable Mapping of Rectal Cancer Whole-Mount Histology with Restaging MRI at Voxel Scale: A Feasibility Study

Purpose To develop a radiology-pathology coregistration method for 1:1 automated spatial mapping between preoperative rectal MRI and ex vivo rectal whole-mount histology (WMH). Materials and Methods This retrospective study included consecutive patients with rectal adenocarcinoma who underwent total neoadjuvant therapy followed by total mesorectal excision with preoperative rectal MRI and WMH from January 2019 to January 2022. A gastrointestinal pathologist and a radiologist established three corresponding levels for each patient at rectal MRI and WMH, subsequently delineating external and internal rectal wall contours and the tumor bed at each level and defining eight point-based landmarks. An advanced deformable image coregistration model based on the linearized iterative boundary reconstruction (LIBR) approach was compared with rigid point-based registration (PBR) and state-of-the-art deformable intensity-based multiscale spectral embedding registration (MSERg). Dice similarity coefficient (DSC), modified Hausdorff distance (MHD), and target registration error (TRE) across patients were calculated to assess the coregistration accuracy of each method. Results Eighteen patients (mean age, 54 years ± 13 [SD]; nine female) were included. LIBR demonstrated higher DSC versus PBR for external and internal rectal wall contours and tumor bed (external: 0.95 ± 0.03 vs 0.86 ± 0.04, respectively, P < .001; internal: 0.71 ± 0.21 vs 0.61 ± 0.21, P < .001; tumor bed: 0.61 ± 0.17 vs 0.52 ± 0.17, P = .001) and versus MSERg for internal rectal wall contours (0.71 ± 0.21 vs 0.63 ± 0.18, respectively; P < .001). LIBR demonstrated lower MHD versus PBR for external and internal rectal wall contours and tumor bed (external: 0.56 ± 0.25 vs 1.68 ± 0.56, respectively, P < .001; internal: 1.00 ± 0.35 vs 1.62 ± 0.59, P < .001; tumor bed: 2.45 ± 0.99 vs 2.69 ± 1.05, P = .03) and versus MSERg for internal rectal wall contours (1.00 ± 0.35 vs 1.62 ± 0.59, respectively; P < .001). LIBR demonstrated lower TRE (1.54 ± 0.39) versus PBR (2.35 ± 1.19, P = .003) and MSERg (2.36 ± 1.43, P = .03). Computation time per WMH slice for LIBR was 35.1 seconds ± 12.1. Conclusion This study demonstrates feasibility of accurate MRI-WMH coregistration using the advanced LIBR method. Keywords: MR Imaging, Abdomen/GI, Rectum, Oncology Supplemental material is available for this article. © RSNA, 2024.

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.

GPT-Driven Radiology Report Generation with Fine-Tuned Llama 3

The integration of deep learning into radiology has the potential to enhance diagnostic processes, yet its acceptance in clinical practice remains limited due to various challenges. This study aimed to develop and evaluate a fine-tuned large language model (LLM), based on Llama 3-8B, to automate the generation of accurate and concise conclusions in magnetic resonance imaging (MRI) and computed tomography (CT) radiology reports, thereby assisting radiologists and improving reporting efficiency. A dataset comprising 15,000 radiology reports was collected from the University of Medicine and Pharmacy of Craiova's Imaging Center, covering a diverse range of MRI and CT examinations made by four experienced radiologists. The Llama 3-8B model was fine-tuned using transfer-learning techniques, incorporating parameter quantization to 4-bit precision and low-rank adaptation (LoRA) with a rank of 16 to optimize computational efficiency on consumer-grade GPUs. The model was trained over five epochs using an NVIDIA RTX 3090 GPU, with intermediary checkpoints saved for monitoring. Performance was evaluated quantitatively using Bidirectional Encoder Representations from Transformers Score (BERTScore), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Bilingual Evaluation Understudy (BLEU), and Metric for Evaluation of Translation with Explicit Ordering (METEOR) metrics on a held-out test set. Additionally, a qualitative assessment was conducted, involving 13 independent radiologists who participated in a Turing-like test and provided ratings for the AI-generated conclusions. The fine-tuned model demonstrated strong quantitative performance, achieving a BERTScore F1 of 0.8054, a ROUGE-1 F1 of 0.4998, a ROUGE-L F1 of 0.4628, and a METEOR score of 0.4282. In the human evaluation, the artificial intelligence (AI)-generated conclusions were preferred over human-written ones in approximately 21.8% of cases, indicating that the model's outputs were competitive with those of experienced radiologists. The average rating of the AI-generated conclusions was 3.65 out of 5, reflecting a generally favorable assessment. Notably, the model maintained its consistency across various types of reports and demonstrated the ability to generalize to unseen data. The fine-tuned Llama 3-8B model effectively generates accurate and coherent conclusions for MRI and CT radiology reports. By automating the conclusion-writing process, this approach can assist radiologists in reducing their workload and enhancing report consistency, potentially addressing some barriers to the adoption of deep learning in clinical practice. The positive evaluations from independent radiologists underscore the model's potential utility. While the model demonstrated strong performance, limitations such as dataset bias, limited sample diversity, a lack of clinical judgment, and the need for large computational resources require further refinement and real-world validation. Future work should explore the integration of such models into clinical workflows, address ethical and legal considerations, and extend this approach to generate complete radiology reports.

Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

Non-operative management of rectal cancer: Highlighting the controversies

There remains much ambiguity on what non-operative management (NOM) of rectal cancer truly entails in terms of the methods to be adopted and the best algorithm to follow. This is clearly shown by the discordance between various national and international guidelines on NOM of rectal cancer. The main aim of the NOM strategy is organ preservation and avoiding unnecessary surgical intervention, which carries its own risk of morbidity. A highly specific and sensitive surveillance program must be devised to avoid patients undergoing unnecessary surgical interventions. In many studies, NOM, often interchangeably called the Watch and Wait strategy, has been shown as a promising treatment option when undertaken in the appropriate patient population, where a clinical complete response is achieved. However, there are no clear guidelines on patient selection for NOM along with the optimal method of surveillance.

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.

Artificial Intelligence in Colorectal Cancer: From Patient Screening over Tailoring Treatment Decisions to Identification of Novel Biomarkers

BACKGROUND

Artificial intelligence (AI) is increasingly entering and transforming not only medical research but also clinical practice. In the last 10 years, new AI methods have enabled computers to perform visual tasks, reaching high performance and thereby potentially supporting and even outperforming human experts. This is in particular relevant for colorectal cancer (CRC), which is the 3rd most common cancer type in general, as along the CRC patient journey many complex visual tasks need to be performed: from endoscopy over imaging to histopathology; the screening, diagnosis, and treatment of CRC involve visual image analysis tasks.

SUMMARY

In all these clinical areas, AI models have shown promising results by supporting physicians, improving accuracy, and providing new biological insights and biomarkers. By predicting prognostic and predictive biomarkers from routine images/slides, AI models could lead to an improved patient stratification for precision oncology approaches in the near future. Moreover, it is conceivable that AI models, in particular together with innovative techniques such as single-cell or spatial profiling, could help identify novel clinically as well as biologically meaningful biomarkers that could pave the way to new therapeutic approaches.

KEY MESSAGES

Here, we give a comprehensive overview of AI in colorectal cancer, describing and discussing these developments as well as the next steps which need to be taken to incorporate AI methods more broadly into the clinical care of CRC.

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.

Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis

Background

Radiomics has recently received considerable research attention for providing potential prognostic biomarkers for locally advanced rectal cancer (LARC). We aimed to comprehensively evaluate the methodological quality and prognostic prediction value of radiomic studies for predicting survival outcomes in patients with LARC.

Methods

The Cochrane, Embase, Medline, and Web of Science databases were searched. The radiomics quality score (RQS), Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist, the Image Biomarkers Standardization Initiative (IBSI) guideline, and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of the selected studies. A further meta-analysis of hazard ratio (HR) regarding disease-free survival (DFS) and overall survival (OS) was performed.

Results

Among the 358 studies reported, 15 studies were selected for our review. The mean RQS score was 7.73±4.61 (21.5% of the ideal score of 36). The overall TRIPOD adherence rate was 64.4% (251/390). Most of the included studies (60%) were assessed as having a high risk of bias (ROB) overall. The pooled estimates of the HRs were 3.14 [95% confidence interval (CI): 2.12-4.64, P<0.01] for DFS and 3.36 (95% CI: 1.74-6.49, P<0.01) for OS.

Conclusions

Radiomics has potential to noninvasively predict outcome in patients with LARC. However, the overall methodological quality of radiomics studies was low, and the adherence to the TRIPOD statement was moderate. Future radiomics research should put a greater focus on enhancing the methodological quality and considering the influence of higher-order features on reproducibility in radiomics.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

OBJECTIVE

To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level.

METHODS

A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated.

RESULTS

We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation.

CONCLUSIONS

The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence.

CLINICAL RELEVANCE STATEMENT

The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.