The Conversion of <scp>MRI</scp> Data With Multiple b‐Values into Signature‐Like Pictures to Predict Treatment Response for Rectal Cancer

Zhu, Hai‐Tao; Zhang, Xiaoyan; Shi, Yan‐Jie; Li, Xiao‐Ting; Sun, Ying‐Shi

doi:10.1002/jmri.28033

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…In general, the critical delineation task is manually performed by experienced oncologists, and the process is quite time‐consuming and tedious 299 . To enhance the efficiency of tumor delineation, various machine learning‐based models and tools have been developed 263,266,268,269,271–276,278–284,300,301 . In a study reported by Lin et al., their machine learning‐assisted delineation tool improved the efficiency by nearly 40% compared to manual delineation 265 .…”

Section: Machine Learning In Multiparametric Mrimentioning

confidence: 99%

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

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Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availabilities of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting.However,the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI.

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Section: Machine Learning In Multiparametric Mrimentioning

confidence: 99%

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

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“…Most studies (15/29) used two or more sequences to build their predictive models. Five studies used only T2WI sequences to construct the models, and the remaining one used DWI sequences ( 44 ). All studies included image slice thicknesses between 2.00mm and 8.0mm.…”

Section: Resultsmentioning

confidence: 99%

“…The 21 included studies were published between 2018 and 2022. More than half of the studies (11/21) were based on a population from China (34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44), three from South Korea (45)(46)(47), three from Italy (48-50), two from the USA (51,52), one from Brazil (53), and one from Belgium (54). Two studies were prospective, and all the remaining studies (19/21) had a retrospective design.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

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

et al. 2022

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PurposeThe 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.MethodsWe 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.ResultsWe 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.ConclusionsRadiomics 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 Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.

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“…Endorectal ultrasound (EUS) affords direct visualization and access for biopsy of rectal cancer and its adjacent lymphadenopathy, which has also been used to predict response to nCRT ( 11 ). MRI may provide exquisite anatomical details on tumor morphology and its association with adjacent structure ( 12 , 13 ). However, these additional imaging modalities are more time-consuming and costly as compared to CT imaging.…”

Section: Introductionmentioning

confidence: 99%

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

Mao

Pei

et al. 2022

Front. Oncol.

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Background and PurposeComputerized 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 MethodsPatients 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.ResultsThe 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.ConclusionOur combined predictive model was robust in differentiating patients with and without response to nCRT.

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The Conversion of MRI Data With Multiple b‐Values into Signature‐Like Pictures to Predict Treatment Response for Rectal Cancer

Cited by 8 publications

References 23 publications

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Artificial intelligence in multiparametric magnetic resonance imaging: A review

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

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

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