Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

Stanzione, Arnaldo; Verde, Francesco; Romeo, Valeria; Boccadifuoco, Francesca; Mainenti, Pier Paolo; Maurea, Simone

doi:10.3748/wjg.v27.i32.5306

Cited by 40 publications

(30 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, the development of radiomics has shown great potential for evaluating therapeutic effects [ 21 ]. By converting medical images into a large number of quantitative features, radiomics can reflect pathophysiology and even tumor heterogeneity [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

et al. 2022

View full text Add to dashboard Cite

Background The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Methods Patients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity. Results Eighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation. Conclusions In rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

et al. 2022

View full text Add to dashboard Cite

show abstract

“…В последнее время быстро развивается область, которая определяет характеристики опухоли с помощью корреляции между радиомическими признаками и генотипом пациента,радиогеномика [20]. В одной из работ показано, что радиомика может помочь в оценке статуса мутации генов, что является важным фактором в прогнозировании ответа рака прямой кишки и ее метастазов на неоадъювантную химиолучевую терапию и стратификации риска опухоли [21].…”

Section: перспективы и преимущества текстурного анализаunclassified

“…Результаты собственного исследования свидетельствуют, что разница в сегментации между двумя врачами значительна при крупных образованиях [3]. Вероятно, ситуация может быть улучшена за счет исследований, направ ленных на выявление радиомических харак теристик, устойчивых к изменчивости сегментирования [21], а также внедрения полуили полностью автоматических методов, которые помогут находить надежные и воспроизводимые автоматические сегменты различных областей интереса [26]. Работ и исследований по предварительному глубокому машинному обучению для создания метода автоматического определения сегментов немало, и они содержат очень хорошие результаты.…”

Section: проблемы внедренияunclassified

Radiomics in liver and pancreatic disorders: a review

et al. 2022

View full text Add to dashboard Cite

A study of the international literature on texture analysis was performed, and the reported data was compared to the findings of radiomics studies performed by the specialists of our institute. The relevant papers were searched using a combination of the following search terms: “radiomics”, “radiology”, “texture analysis”, “perspectives”, and “clinical implementation”. The search was limited to papers published in English within the last 5 years, which essentially focused on liver and pancreas disorders. Due to the publication of new data on a fairly daily basis, the topic has not lost its relevance. The vast majority of authors confirm that radiomics can be efficiently used during diagnosis, treatment planning, and patient monitoring. However, consensus on the implementation of radiomics has not been reached yet, thereby delaying its introduction into clinical practice. The data collected in our institution reports that the clinical application of texture analysis methods may be very promising.

show abstract

“…As a quantitative analysis approach, radiomics can convert image data of ROI into high-resolution, discoverable feature space data using automated data characterization algorithms, and finally achieves deeper analysis and application of information [10]. It has been widely used in oncologic imaging, including differential diagnosis, tumor staging, genotyping, treatment response, and patient survival prediction [11][12][13]. It is worth mentioning that due to the qualitative and quantitative problems in the conventional imaging diagnosis of nonneoplastic lesions, radiomics researches of non-neoplastic diseases also have important clinical value.…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics signature for identification of invisible basal cisterns changes in tuberculous meningitis: a preliminary multicenter study

Liu

et al. 2022

Eur Radiol

View full text Add to dashboard Cite

Objective To develop and evaluate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for identification of invisible basal cisterns changes in tuberculous meningitis (TBM) patients. Methods Our retrospective study enrolled 184 TBM patients and 187 non-TBM controls from 3 Chinese hospitals (training dataset, 158 TBM patients and 159 non-TBM controls; testing dataset, 26 TBM patients and 28 non-TBM controls). nnU-Net was used to segment basal cisterns in fluid-attenuated inversion recovery (FLAIR) images. Subsequently, radiomics features were extracted from segmented basal cisterns in FLAIR and T2-weighted (T2W) images. Feature selection was carried out in three steps. Support vector machine (SVM) and logistic regression (LR) classifiers were applied to construct the radiomics signature to directly identify basal cisterns changes in TBM patients. Finally, the diagnostic performance was evaluated by the receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). ResultsThe segmentation model achieved the mean Dice coefficients of 0.920 and 0.727 in the training and testing datasets, respectively. The SVM model with 7 T2WI-based radiomics features achieved best discrimination capability for basal cisterns changes with an AUC of 0.796 (95% CI, 0.744-0.847) in the training dataset, and an AUC of 0.751 (95% CI, 0.617-0.886) with good calibration in the testing dataset. DCA confirmed its clinical usefulness. Conclusion The T2WI-based radiomics signature combined with deep learning segmentation could provide a fully automatic, non-invasive tool to identify invisible changes of basal cisterns, which has the potential to assist in the diagnosis of TBM. Key Points• The T2WI-based radiomics signature was useful for identifying invisible basal cistern changes in TBM.• The nnU-Net model achieved acceptable results for the auto-segmentation of basal cisterns.• Combining radiomics and deep learning segmentation provided an automatic, non-invasive approach to assist in the diagnosis of TBM.

show abstract

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

Cited by 40 publications

References 117 publications

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

Radiomics in liver and pancreatic disorders: a review

MRI-based radiomics signature for identification of invisible basal cisterns changes in tuberculous meningitis: a preliminary multicenter study

Contact Info

Product

Resources

About