Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: Comparison with tumour regression grade at final histology

Giganti, Francesco; Marra, Paolo; Ambrosi, Alessandro; Salerno, Annalaura; Antunes, Sofia; Chiari, Damiano; Orsenigo, E.; Esposito, Antonio; Mazza, Elena; Albarello, Luca; Nicoletti, Roberto; Staudacher, C.; Maschio, Alessandro Del; Cobelli, Francesco De

doi:10.1016/j.ejrad.2017.02.043

Cited by 57 publications

(46 citation statements)

References 25 publications

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“…Radiomics – the high-throughput extraction of quantitative imaging features characterizing the spatial relationships and consistency of signal intensities – within the tumor region has demonstrated the ability to predict treatment response across a range of cancer types and imaging modalities ( 14 ). In the realm of gastric cancer, radiomics of CT has been verified as a potential preoperative prognostic biomarker in gastric cancer ( 15 ); pre-treatment CT texture analysis can potentially provide important information regarding the response rate to neoadjuvant therapy for gastric cancer, thereby improving risk stratification ( 16 ). Therefore, radiomics may be used as a crucial marker for the response to neoadjuvant therapy in locally advanced gastric cancer.…”

Section: Introductionmentioning

confidence: 99%

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

Zhang

Dai

et al. 2018

Chinese Journal of Cancer Research

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ObjectiveThe standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography (CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy.MethodsThirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase (AP) and portal venous phase (PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient. Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic (ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response (non-GR) based on tumor regression grade (TRG).ResultsThe mean area under the curve (AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC (>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs (all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP (AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity, 0.889; Z=2.039; P=0.041).ConclusionsIt is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT.

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Section: Introductionmentioning

confidence: 99%

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

Zhang

Dai

et al. 2018

Chinese Journal of Cancer Research

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“…Giganti et al also assessed pre chemotherapy texture features derived from the late arterial phase of 34 patients. They found entropy and compactness were higher and uniformity lower in responders [43]. No studies have assessed prognostication or response to therapy in gastric cancer.…”

Section: Ct Radiomicsmentioning

confidence: 99%

Radiomics in esophageal and gastric cancer

et al. 2018

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Esophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multimodality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi-modality therapy, there is a clear need to improve patient stratification. Radiomic approaches provide an opportunity to improve tumor phenotyping. In this review we assess the evidence to date and discuss how these approaches could improve outcome in esophageal, esophago-gastric, and gastric cancer.

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“…)YearCountryType of studyNo. of patientsROI selectionCE-MDCT phaseReference standardTexture featuresKey messageBa-Ssalamah et al [59]2013AustriaRetrospective47Single sliceArterial; venousBiopsy2nd order statisticsTexture analysis can differentiate histological subtypesGiganti et al [62]2016ItalyRetrospective56Whole tumourLate arterialRadical resection1st and 2nd order statisticsTexture analysis reflects tumour aggressiveness and is related to prognosisYoon et al [63]2016KoreaRetrospective26Single sliceVenousBiopsy2nd order statisticsTexture features are associated with better survival in HER2-positive patients receiving trastuzumabGiganti et al [61]2017ItalyRetrospective34Whole tumourLate arterialRadical resection1st and 2nd order statisticsTexture analysis provides important information on the response rate to neoadjuvant therapyLiu et al [60]2017ChinaRetrospective107Single sliceArterial; venousRadical resection1st and 2nd order statisticsTexture parameters can predict the degree of differentiation, Lauren classification and vascular invasion ROI region of interest, CE-MDCT contrast-enhanced multidetector computed tomography, HER human epidermal growth factor receptor…”

Section: Ce-mdct and Imaging Biomarkersmentioning

confidence: 99%

“…Similar results have been published by Liu et al [60]; the authors found that texture parameters can predict the degree of differentiation, Lauren classification and vascular invasion. There is also evidence that textural features can predict treatment response [61] when the response rate is based on TRG [49]. Like DW-MRI, quantitative imaging from texture analysis is associated with the prognosis of untreated [62] and treated [63] gastric cancer.…”

Section: Ce-mdct and Imaging Biomarkersmentioning

confidence: 99%

Gastric cancer and imaging biomarkers: Part 1 – a critical review of DW-MRI and CE-MDCT findings

2018

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The current standard of care for gastric cancer imaging includes heterogeneity in image acquisition techniques and qualitative image interpretation. In addition to qualitative assessment, several imaging techniques, including diffusion-weighted magnetic resonance imaging (DW-MRI), contrast-enhanced multidetector computed tomography (CE-MDCT), dynamic-contrast enhanced MRI and 18F-fluorodeoxyglucose positron emission tomography, can allow quantitative analysis. However, so far there is no consensus regarding the application of functional imaging in the management of gastric cancer. The aim of this article is to specifically review two promising biomarkers for gastric cancer with reasonable spatial resolution: the apparent diffusion coefficient (ADC) from DW-MRI and textural features from CE-MDCT. We searched MEDLINE/ PubMed for manuscripts published from inception to 6 February 2018. Initially, we searched for (gastric cancer OR gastric tumour) AND diffusion weighted magnetic resonance imaging. Then, we searched for (gastric cancer OR gastric tumour) AND texture analysis AND computed tomography. We collated the results from the studies related to this query. There is evidence that: (1) the ADC is a promising biomarker for the evaluation of the aggressiveness (T and N stage), treatment response and prognosis of gastric cancer; (2) textural features are related to the degree of differentiation, Lauren classification, treatment response and prognosis of gastric cancer. We conclude that these imaging biomarkers hold promise as effective additional tools in the diagnostic pathway of gastric cancer and may facilitate the multidisciplinary work between the radiologist and clinician, and across different institutions, to provide a greater biological understanding of gastric cancer.Key Points • Quantitative imaging is the extraction of quantifiable features from medical images for the assessment of normal or pathological conditions and represents a promising area for gastric cancer. • Quantitative analysis from CE-MDCT and DW-MRI allows the extrapolation of multiple imaging biomarkers. • ADC from DW-MRI and CE- MDCT-based texture features are non-invasive, quantitative imaging biomarkers that hold promise in the evaluation of the aggressiveness, treatment response and prognosis of gastric cancer.

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Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: Comparison with tumour regression grade at final histology

Cited by 57 publications

References 25 publications

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

Radiomics in esophageal and gastric cancer

Gastric cancer and imaging biomarkers: Part 1 – a critical review of DW-MRI and CE-MDCT findings

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