CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes

Lubner, Meghan G.; Stabo, Nicholas; Lubner, Sam Joseph; Río, Alejandro Muñoz del; Song, C.; Halberg, Richard B.; Pickhardt, Perry J.

doi:10.1007/s00261-015-0438-4

Cited by 250 publications

(183 citation statements)

References 28 publications

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“…Despite the need for further validation sets, this study provides a basis for additional research (e.g using PET-CT features) that could improve performance. We believe such radiomics based analyses can be used as a complementary method of patient stratification for NSCLC prior to the initiation of therapy as is currently being investigated in other disease sites such as breast [35,36], colorectal cancer [37], and glioblastoma [38]. …”

Section: Discussionmentioning

confidence: 99%

Radiomic phenotype features predict pathological response in non-small cell lung cancer

Coroller

Agrawal

Narayan

et al. 2016

Radiotherapy and Oncology

282

196

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Background and purpose Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC). Materials and Methods 127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison. Results Seven features were predictive for pathologic gross residual disease (AUC > 0.6, p-value < 0.05), and one for pathologic complete response (AUC = 0.63, p-value = 0.01). No conventional imaging features were predictive (range AUC = 0.51–0.59, p-value > 0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present rounder shape (spherical disproportionality, AUC = 0.63, p-value = 0.009) and heterogeneous texture (LoG 5 mm 3D - GLCM entropy, AUC = 0.61, p-value = 0.03). Conclusion We identified predictive radiomic features for pathological response, although no conventional features were significantly predictive. This study demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.

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Section: Discussionmentioning

confidence: 99%

Radiomic phenotype features predict pathological response in non-small cell lung cancer

Coroller

Agrawal

Narayan

et al. 2016

Radiotherapy and Oncology

282

196

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“…However, several studies have shown that use of a single slice is sufficient for sampling and extracting subtle features relevant for the clinical application being evaluated [22,40]. In addition, the cohort was a pooled group of liver disease with a variety of etiologies included.…”

Section: Discussionmentioning

confidence: 99%

“…This additional evaluation of segments I-III versus IV-VIII was based on the morphologic changes seen in cirrhosis and prior work looking at the liver segmental volume ratio (LSVR) [15,16]. Previous studies have demonstrated that CTTA from a single-slice analysis have demonstrated the ability to extract sufficient information related to answer the clinical question and may not require multi-slice or volume analysis which have not shown to add significant benefit to the clinical application being researched (22,36). Furthermore single-slice analysis reduces the computational time (is more practical in a routine radiological workflow) and complexities associated with multi-slice/volumetric analysis related to operator variability associated with ROI drawing particularly if the ROI drawing process involves manual/semi-automated segmentation approaches.…”

Section: Ct Texture Analysis (Ctta)mentioning

confidence: 99%

“…CT texture analysis is an emerging technique that quantifies heterogeneity of a region of interest by analyzing the distribution and/or relationship of pixel or voxel gray levels in the image [17]. This technique has been most intensely studied in oncologic applications, and has shown association with pathologic features and clinical outcomes in a variety of tumor types [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Some non-oncologic applications are being evaluated, with several groups using texture analysis to assess emphysema and fibrosis in the lung [32,33].…”

Section: Introductionmentioning

confidence: 99%

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Texture analysis of the liver at MDCT for assessing hepatic fibrosis

et al. 2017

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Abstract:Purpose: To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0-F4)Methods: Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially-available software (TexRAD), which uses a filtration histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I-III were obtained. CTTA parameters were correlated against fibrosis stage (F0-F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1-F3). Results:The study cohort consisted of 289 adults (158M/131W; mean age, 51 yrs), including healthy controls (F0, n=77), and patients with increasing stages of fibrosis (F1, n=42; F2 n=37; F3 n=53; F4 n=80). Mean of the pixel histogram increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cut-off 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71-0.73 across medium and coarse filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87 respectively at coarse filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3. Conclusion:CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.

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“…TA has been used to distinguish gastric cancer subtypes 19 and to predict OS in patients with primary colorectal cancers, 20 hepatocellular carcinoma, 21 and CRLM. 22 In patients with CRLM, tumor morphology assessed by radiologists correlated with pathologic response and survival, suggesting a link between imaging, pathology, and survival, but this relationship has not been well-elucidated. 23 …”

Section: Introductionmentioning

confidence: 99%

Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer

et al. 2017

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Background Recurrence after resection of colorectal liver metastases (CRLMs) occurs in up to 75% of patients. Preoperative prediction of hepatic recurrence may inform therapeutic strategies at the time of initial resection. Texture analysis (TA) is an established technique that quantifies pixel intensity variations (heterogeneity) on cross-sectional imaging. We hypothesized that tumoral and parenchymal changes that are predictive of overall survival (OS) and recurrence in the future liver remnant (FLR) can be detected using TA on preoperative computed tomography images. Methods Patients who underwent resection for CRLM between 2003 and 2007 with appropriate preoperative computed tomography scans were included (n=198) in this retrospective study. Texture features extracted from the tumor and FLR and clinicopathologic variables were incorporated into a multivariable survival model. Results Quantitative imaging features of the FLR were an independent predictor of both OS and hepatic disease-free survival. Tumor texture showed significant association with OS. TA of the FLR allowed patient stratification into two groups with significantly different risks of hepatic recurrence (HR, 2.09; 95% CI, 1.33–3.28; p=0.001). Patients with homogeneous parenchyma had approximately twice the risk of hepatic recurrence (41% vs. 20%). Conclusion TA of the tumor and FLR are independently associated with OS. TA of the FLR is independently associated with HDFS. Patients with homogeneous parenchyma had a significantly higher risk of hepatic recurrence. Preoperative TA of the liver represents a potential biomarker to identify patients at risk of liver recurrence after resection for CRLM.

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CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes

Abstract: CT texture features, particularly entropy, MPP, and SD, are significantly associated with tumor grade in untreated CRC liver metastases. Tumor entropy at coarse filters correlates with overall survival. Single-slice 2D texture analysis appears to be adequate.

Cited by 250 publications

References 28 publications

Radiomic phenotype features predict pathological response in non-small cell lung cancer

Radiomic phenotype features predict pathological response in non-small cell lung cancer

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer

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