Haralick’s texture features for the prediction of response to therapy in colorectal cancer: a preliminary study

Caruso, Damiano; Zerunian, Marta; Ciolina, Maria; Santis, Domenico De; Rengo, Marco; Soomro, Mumtaz Hussain; Giunta, Gaetano; Conforto, Silvia; Schmid, Maurizio; Neri, Emanuele; Laghi, Andréa

doi:10.1007/s11547-017-0833-8

Cited by 39 publications

(24 citation statements)

References 29 publications

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“…Different types of individual radiomic features were associated with complete pathologic response to CRT [ 37 , 39 – 45 , 47 , 63 ]. The predictive value of entropy, kurtosis, skewness, and tumor volume were most frequently described [ 37 , 40 – 44 ]. Two studies reported no significant association between PET texture features and pathologic response to CRT in patients with LARC [ 38 , 46 ].…”

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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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%

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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“…Radiomics analysis may provide information, which could impact overall patient prognosis and potential eligibility for target therapies [ 72 ]. A combined model, including the radiomics and clinical features obtained from pretreatment T2-weighted MR images, was able to predict the likelihood of developing distant metastases, thus potentially aiding in tailoring treatment strategies in high-risk patients [ 73 ].…”

Section: Radiomics For the Personalized Management Of Rc Patients: Current Evidence And Perspectivesmentioning

confidence: 99%

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

et al. 2021

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While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

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“…It suggested that the Kurtosis value of T2WI before treatment could be used as a predictor of the therapeutic effect of nCRT in rectal cancer patients and help physicians guide the follow-up clinical decision-making as well. Another study [19] showed that the Contrast and Entropy of T2WI before treatment could also predict the therapeutic effect of nCRT for CRC.…”

Section: Prediction Of Therapeutic Effect Of Primary Lesionsmentioning

confidence: 99%

“…In addition to the wide applications in MRI, texture analysis technology has been likewise used in CT [19,21] and Pet-ct [22] images to predict the therapeutic effect of nCRT for CRC. Caruso et al [19] found that five texture parameters, including the Energy, Contrast and Correlation and so on, could be used as imaging markers to predict the sensitivity of nCRT in CRC patients.…”

Section: Prediction Of Therapeutic Effect Of Primary Lesionsmentioning

confidence: 99%

Application and Progress of Texture Analysis in the Therapeutic Effect Prediction and Prognosis of Neoadjuvant Chemoradiotherapy for Colorectal Cancer

Wang

Jin

2019

Chinese Medical Sciences Journal

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Colorectal cancer is one of the most common malignant tumors, and the morbidity and mortality are increasing gradually over the last years in China. Neoadjuvant chemoradiotherapy (nCRT) is currently applied to the treatment of colorectal cancer patients, and it is helpful to improve the prognosis. The sensitivity of patients to nCRT is different due to individual differences. Predicting the therapeutic effect of nCRT is of great importance for the further treatment methods. Texture analysis, as an image post-processing technique, has been more and more utilized in the field of oncologic imaging. This article reviews the application and progress of texture analysis in the therapeutic effect prediction and prognosis of nCRT for colorectal cancer.

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Haralick’s texture features for the prediction of response to therapy in colorectal cancer: a preliminary study

Abstract: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients.

Cited by 39 publications

References 29 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

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

Application and Progress of Texture Analysis in the Therapeutic Effect Prediction and Prognosis of Neoadjuvant Chemoradiotherapy for Colorectal Cancer

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