Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection

Abstract

The value of image based texture features as a powerful method to predict prognosis and assist clinical management in cancer patients has been established recently. However, texture analysis using histograms and grey-level co-occurrence matrix in pancreas cancer patients has rarely been reported. We aimed to analyze the association of survival outcomes with texture features in pancreas head cancer patients. Eighty-eight pancreas head cancer patients who underwent preoperative CT images followed by curative resection were included. Texture features using different filter values were obtained. The texture features of average, contrast, correlation, and standard deviation with no filter, and fine to medium filter values as well as the presence of nodal metastasis were significantly different between the recurred (n = 70, 79.5%) and non-recurred group (n = 18, 20.5%). In the multivariate Cox regression analysis, lower standard deviation and contrast and higher correlation with lower average value representing homogenous texture were significantly associated with poorer DFS (disease free survival), along with the presence of lymph node metastasis. Texture parameters from routinely performed pre-operative CT images could be used as an independent imaging tool for predicting the prognosis in pancreas head cancer patients who underwent curative resection.

Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy

“…For example, a 2019 study identified a specific radiomic signature of pancreatic cancer that correlated with overall survival and local control after treatment with stereotactic body radiation therapy [8]. Another study in 2018 analyzed texture features of tumors of the pancreatic head, finding that some features (such as certain filter values and contrast) served as independent prognostic factors in predicting decreased disease free survival [9]. These studies, and others like them, illustrate the ability of radiomic data to provide novel behavioral and prognostic information about individual pancreatic cancers that would not otherwise be discoverable by conventional methods of evaluation.…”

Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma

“…For example, the entropy and skewness were independently associated with OS in HNSCC patients undergoing TPF chemotherapy [15]. In histogram analysis, a pixel distribution with higher kurtosis, energy and entropy, and a positive or negative skewness indicated the enhancement of tumors heterogeneity [31,32]. The high homogeneity of PET-CT images also was revealed as predictors of progression-free survival in pharynx cancer [33].…”

Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma

“…Gray level co-occurrence matrix is a texture analysis form that provides statistical measurement of spatial relationship of pixels in images, 11 whilst the run length matrix is the length of the continuum element with the same gray level in the preset direction. 42 The grayscale run length is only a measurement of image pixel information.…”

“…"Radiomics", which is de ned as the high-throughput extraction of image features from radiographic images, has potential to provide a detailed pre-operative evaluation method of tumor heterogeneity. [11][12][13][14][15][16][17] This method of imaging analysis utilizes algorithms to derive image texture. At present, the most commonly used image texture analysis methods are rst and second order statistical method analyses of image pixels and their neighborhood gray level.…”