Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients

Shayesteh, Sajad P.; Alikhassi, Afsaneh; Esfahani, A Fard; Miraie, M.; Geramifar, Parham; Bitarafan-Rajabi, Ahmad; Haddad, Peiman

doi:10.1016/j.ejmp.2019.03.013

Cited by 43 publications

(31 citation statements)

References 43 publications

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“…Moreover, improved predictive performance was obtained compared to the LR model based on TNM staging [ 48 ]. Shayesteh et al analyzed machine learning classifiers individually and together for response prediction and reported best predictive performance for the ensemble of machine learning models [ 57 ]. Two studies assessed the added value of radiomics models in addition to radiologists assessment.…”

Section: Resultsmentioning

confidence: 99%

“…Internal validation was performed by applying resampling methods in 24/60 (40%) of studies, such as bootstrapping and cross-validation. However, only 15/60 (25%) studies performed validation in an external dataset [ 20 , 22 , 26 , 27 , 32 , 34 , 49 , 50 , 57 , 60 – 62 , 64 , 66 , 76 ]. Five points were subtracted if studies lacked external validation, as this is one of the most important components for model generalizability.…”

Section: Resultsmentioning

confidence: 99%

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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Wesdorp

Hellingman

Jansma

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Wesdorp

Hellingman

Jansma

et al. 2020

Eur J Nucl Med Mol Imaging

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“…The majority of studies focused on predicting the pathological complete response, using a single MRI sequence (T2-WI or apparent diffusion coefficient (ADC) maps) or a multiparametric approach [ 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. Additionally, there are several studies that investigated the performance of radiomic features to discriminate good responders [ 56 , 57 , 58 , 59 , 60 ]. In contrast to our research, these investigations either used other pathologic classifications for the quantification of tumor regression grade such as Dworak [ 57 ] or Mandard [ 59 ] or the authors divided their study population different from our approach, including in the non-responders’ group both patients with a TRG score of 2 and 3 [ 56 , 58 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, there are several studies that investigated the performance of radiomic features to discriminate good responders [ 56 , 57 , 58 , 59 , 60 ]. In contrast to our research, these investigations either used other pathologic classifications for the quantification of tumor regression grade such as Dworak [ 57 ] or Mandard [ 59 ] or the authors divided their study population different from our approach, including in the non-responders’ group both patients with a TRG score of 2 and 3 [ 56 , 58 , 60 ]. In our research, we used the Ryan TRG classification and we considered patients with a TRG score of 3 as non-responders, while patients with TRG 2 were included in the responders’ group.…”

Section: Discussionmentioning

confidence: 99%

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Petresc

Lebovici

Caraiani

et al. 2020

Cancers

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Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

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“…Shayesteh et al. (2019) compared four machine learning methods (SVM, ANN, BN and KNN) and model based on different combinations of the four machine learning methods on a data set of features (shape, intensity, texture, …), extracted from magnetic resonance imaging (MRI) and recorded on 98 patients with rectal cancer to predict their chemotherapy response. The authors showed that the model that combines the four methods has the highest performance (AUC greater than 0.9 and accuracy greater than 82%).…”

Section: Review On Machine Learning Applications For Predictive Data mentioning

confidence: 99%

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Jamin

Ábrahám

Humeau-Heurtier

2020

Clin Physio Funct Imaging

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With the evidence-based medicine, doctors have settings and data from which they can evaluate the risk-benefit ratio of a treatment. They can also analyse different information to make risk-based choices. With the emergence of new technologies in medicine, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. However, the collection, storage and use of data raise ethical and security issues. Internet of things (IoT) is a field that groups all the systems that can be identified, perform measurements and do tasks while being open on the Internet. It is an emerging field (Stojkoska & Trivodaliev, 2017; Whitmore et al., 2015) that leads to various IoT applications in healthcare (Islam et al., 2015; Yin et al., 2016). Pavel et al. (2013) proposed to divide the future of IoT health care into four parts: patient-based applications, physician-based applications, treatment and prevention

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Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients

Cited by 43 publications

References 43 publications

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

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