MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases

Zhang, Huan; Li, Wenhua; Hu, Feixiang; Sun, Yueming; Hu, Tingdan; Tong, Tong

doi:10.1007/s00261-018-1682-1

Cited by 51 publications

(40 citation statements)

References 26 publications

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“…Most papers ( n = 28) described retrospective analyses, while four reported planned secondary analyses of prospectively acquired data [ 32 , 33 , 34 , 35 ]. Nineteen authors analyzed computed tomography (CT) [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], eight magnetic resonance imaging (MRI) [ 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], three positron-emission tomography (PET)/CT [ 59 , 60 , 61 ], and two multiple imaging modalities (CT and MRI; PET and MRI, respectively) [ 62 , 63 ]. Various software applications were used for texture analysis, with these being custom-made in a large proportion of cases ( n = 10).…”

Section: Resultsmentioning

confidence: 99%

“…Ten studies analyzed the association between radiomic features and response to chemotherapy [ 32 , 33 , 37 , 38 , 40 , 45 , 46 , 54 , 56 , 61 ]. Evidence mainly regarded patients receiving anti-VEGF treatment.…”

Section: Resultsmentioning

confidence: 99%

“…The reference standard was RECIST criteria in all but one study, which used tumor regression grade (TRG) [ 45 ]. Half of the studies analyzed the imaging modalities before and after treatment [ 32 , 33 , 40 , 45 , 46 ], while the other half considered only baseline imaging [ 37 , 38 , 54 , 56 , 61 ]. Four studies focused on targeted therapies associated with systemic chemotherapy [ 32 , 33 , 45 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

“…In the study by Beckers et al, treatment success was slightly associated with higher entropy (6.65 ± 0.26 in responders vs. 6.51 ± 0.34 in non-responders, p = 0.08) [ 38 ]. The Zhang et al, analysis of T2 MRI images before chemotherapy showed that responding lesions had a higher variance and lower angular second moment (two measures of homogeneity) than non-responding ones (variance: 446.07 ± 329.60 in responders vs. 210.23 ± 183.39 in non-responders, p < 0.001; angular second moment: 0.96 ± 0.02 vs. 0.98 ± 0.01, respectively, p < 0.001) [ 56 ]. Dercle et al, built a signature, based on two measures of entropy, gray-tone difference matrix contrast and shape, which allowed to predict responsiveness to anti-angiogenic treatment (AUC = 0.80, CI95% = 0.69–0.94 for patients with high imaging quality; AUC = 0.72, CI95% = 0.59–0.83 for patients with standard imaging quality) [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

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Radiomics of Liver Metastases: A Systematic Review

Fiz

Viganò

Gennaro

et al. 2020

Cancers

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Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Radiomics of Liver Metastases: A Systematic Review

Fiz

Viganò

Gennaro

et al. 2020

Cancers

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“…Liang et al [29] found that the value of Mean from the ADC maps of the responding group were significantly lower than that of the non-responding group in colorectal liver metastases. A latest study [30] exhibited that the higher Entropy, Contrast and the lower Correlation were independently associated with a better chemotherapeutic response in colorectal liver metastases.…”

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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Deep learning‐assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases

Zhu

et al. 2020

Intl Journal of Cancer

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Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well‐recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic resonance imaging (MRI) to predict pathological response in CRLM. CRLM patients from center one who had ≤5 lesions and were scheduled to receive preoperative chemotherapy followed by liver resection between January 2013 and November 2016, were included prospectively and chronologically divided into a training cohort (80% of patients) and a testing cohort (20% of patients). Patients from center two were included January 2017 and December 2018 as an external validation cohort. MRI‐based models were constructed to discriminate according to pathology tumor regression grade (TRG) between the response (TRG1/2) and nonresponse (TRG3/4/5) groups at the lesion level. From center one, 155 patients (328 lesions) were included; chronologically, 101 (264 lesions) in the training cohort and 54 (64 lesions) in the testing cohort. The model achieved better accuracy (0.875 vs 0.578) and AUC (0.849 vs 0.615) than RECIST for discriminating response; it also distinguished the survival outcomes after hepatectomy better than the RECIST criteria. Evaluations of the external validation cohort (25 patients, 61 lesions) also showed good ability with an AUC of 0.833. In conclusion, the MRI‐based deep learning model provided accurate prediction of pathological tumor response to preoperative chemotherapy in patients with CRLM and may inform individualized treatment.

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MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases

Abstract: MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.

Cited by 51 publications

References 26 publications

Radiomics of Liver Metastases: A Systematic Review

Radiomics of Liver Metastases: A Systematic Review

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

Deep learning‐assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases

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