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Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator – Feedback Plc (FDBK)
Paper published in Scientific Reports (“Nature” affiliated journal) by Professor Shigeru Kiryu, Department of Radiology, International University of Health and Welfare Hospital, Nasushiobara, Tochigi, Japan.
We assessed the relationship between the heterogeneity of HCC on preoperative non-contrast-enhanced CT and patient prognosis. The heterogeneity of CT images from 122 patients was assessed and texture feature parameters such as mean, standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis were obtained using filtration. The relationship between CT texture features and 5-year overall survival (OS) or disease-free survival (DFS) was assessed. Multivariate regression analysis was performed to evaluate the independence of texture feature from clinical or pathological parameters. The Kaplan-Meier curves for OS or DFS was significantly different between patient groups dichotomized by cut-off values for all CT texture parameters with filtration at at least one filter level. Multivariate regression analysis showed the independence of most CT texture parameters on clinical and pathological parameters for OS with filtration at at least one filter level and without filtration except kurtosis. SD, entropy, and MPP with coarse filter, and skewness without filtration showed a significant correlation for DFS. CT texture features of non-contrast-enhanced CT images showed a relationship with HCC prognosis. Multivariate regression analysis showed the possibility of CT texture feature increase the prognostic prediction of HCC by clinical and pathological information.
The incidence of hepatocellular carcinoma (HCC) has increased due to the spread of hepatitis, and HCC is now the second leading cause of cancer-related death worldwide1. Because HCC has a high potential for vascular invasion, metastasis, and recurrence, its prognosis is poor. Among several therapies used to treat HCC, hepatic resection is considered the mainstay of curative therapy for localized HCC2,3. However, due to its high rate of recurrence after resection, preoperative prognostic prediction of HCC is important for the appropriate management of patients.
Heterogeneity is widely recognized as a feature of malignancy associated with adverse tumor biology, and it is assessed on medical images using a texture-analysis technique4. On computed tomography (CT), photon noise, caused by fluctuations in the number of photons in incident X-rays, obscures biologic heterogeneity4. CT texture analysis using filters to reduce photon noise is used for a variety of purposes, including assessment of malignant tumor prognosis5.
CT is widely used for the detection and assessment of HCC and is an indispensable preoperative examination. If information related to HCC prognosis is available from non-contrast-enhanced CT images, preoperative CT can provide additional meaningful data. The objective of this study was to assess the relationship between HCC heterogeneity and prognosis after the resection of HCC using non-contrast-enhanced CT texture analysis with a filter technique.
Materials and Methods
This study was approved by the Research Ethics Committee of our institution as a retrospective data analysis for medical imaging-based diagnoses, and the requirement for informed consent was waived by the Committee (26-73-1126). Additionally, all experiments and methods in this study were performed in accordance with the Declaration of Helsinki.
PatientsA total of 169 consecutive patients who underwent initial hepatectomy for HCC between January 2004 and September 2009 were included in this study. This study was approved by the Research Ethics Committee of the institution. A total of 16 patients were excluded due to the unavailability of CT images on the picture archiving system. We excluded a further 28 patients who underwent transcatheter arterial embolization (TACE) prior to CT, because the high density of deposited iodized oil may affect the texture features. A total of three patients with lesions too small (<1 cm) to evaluate on CT were additionally excluded. Finally, the study group consisted of 122 patients: 92 males (mean age, 63.5 years; range, 25–82 years) and 30 females (mean age, 69.2 years; range, 53–81 years). A total of 37 patients underwent TACE between CT and surgery (Table 1). Clinical patient’s information such as Child-Pugh score, serum levels of alpha-fetoprotein (AFP), tumor differentiation, pathological stage (pStage), or presence/absence of venous invasion was examined using database of surgical records.
CT studies were performed using a 4-, 16-, or 64-detector row CT scanner (Light Speed QX/I, LightSpeed Ultra, LightSpeed VCT, GE Medical Systems, Milwaukee, WI, USA) or a 16-detector row CT scanner (Aquilion 16, Toshiba Medical Systems Corp., Tochigi, Japan). The same clinical protocol was used: 120 kV, 180–280 mAs depending on body habitus, a matrix of 512, and a field of view of 350–400 mm. Non-contrast CT was performed using a 5-mm contiguous axial section to encompass the whole liver. Non-contrast CT was mostly performed as part of the contrast-enhanced study.
The location of the HCC was defined on unenhanced CT according to the surgical records, and the slice with the largest lesion was selected by one of the research conductors (a radiologist with 22 years of experience with abdominal CT). Images were loaded onto a workstation for further texture analysis. Texture analysis was performed by a single observer (a radiologist with 12 years of experience with abdominal CT) who was blinded to the clinical outcome, using TexRAD (TexRAD Ltd., www.texrad.com part of Feedback Plc., Cambridge, UK), a proprietary research software algorithm developed to visualize and quantify the texture properties of tissues from medical imaging scans. The region of interest (ROI) was initially delineated around the tumor periphery section by an observer and refined by excluding areas of air using a thresholding procedure that removed any pixels with attenuation values below −50 Hounsfield unit from the analysis. The assessment of texture feature comprised an initial filtration step in which a Laplacian of Gaussian spatial band-pass filter was used to selectively extract features of different sizes and intensity variations, at three different frequency scales: fine (features approximately 2 mm in width), medium (features approximately 4 mm in width) and coarse (features approximately 6 mm in width) (Fig. 1). The heterogeneity within the ROI was assessed with and without filtration, and texture parameters were calculated as follows: the mean grey-level intensity (mean), variation/dispersion from the mean grey-level intensity (standard deviation, SD), the irregularity or complexity of the grey signal (entropy), the average intensity of the positive grey-level signal pixel values within the ROI (mean of the positive pixels, MPP), asymmetry of the distribution (skewness), and pointiness or peakedness of the distribution (kurtosis)4,5. These parameters derived from the histogram analysis. SD, kurtosis and skewness describe the shape of the histogram representing the gray-level variation, asymmetry and peak with in the ROI, respectively. Entropy is a measure of texture irregularity and defined by following equation where l is the pixel levels (between l = 1 to k) in ROI, and p(l) is the probability of the occurrence of that pixel level.
The relationship between the CT texture features and 5-year overall survival (OS) or disease-free survival (DFS) was assessed by Kaplan-Meier analyses. Patients were dichotomized according to the best cut-off values, which were calculated by the log-rank test to classify the outcome of OS or DFS.
The multivariate Cox proportional hazard regression analysis using likelihood ratio test was performed to evaluate the independence of CT texture feature from clinical and pathological parameters, such as the Child-Pugh score, serum levels of AFP, tumor histological differentiation, pStage, or presence/absence of venous invasion, on OS or DFS. The correlations between CT texture features and clinical parameters were assessed: presence of HBs-Ag or HCV-Ab using paired-t-test; Child-Pugh classification, differentiation of HCC, or TNM stage using Spearman’s correlation; AFP using Pearson’s correlation. A P-value < 0.05 was considered to indicate statistical significance. The correlations of the CT texture features were assessed using the Spearman rank correlation coefficient. All statistical analyses were performed by a statistician using SPSS (version 18.0; IBM Corp, Armonk, NY, USA) statistical software.
Of the total number of patients, 12 were stage I, 60 stage II, 35 stage III, 12 stage IVA, and 3 stage IVB (Table 1). A total of 60 patients had a Child-Pugh score of 5, 46 a score of 6, 13 a score of 7, 2 a score of 8, and 1 a score of 9.Patients with the Child-Pugh score of 8–9 had inherited constitutional jaundice. AFP levels above 400 ng/ml were observed in 22 (18%) patients and venous invasion in 38 (31%) patients. A total of 77 (63.1%) patients had a single HCC, 24 (19.7%) patients had two HCCs, and 21 (17.2%) patients had three or more HCCs. The histopathologic differentiation of the tumors was well in 26 (21.3%) patients, moderate in 82 (67.2%) patients, poor in 10 (8.2%) patients and necrosis in 4 (3.3%) patients. Sixty-two (50.8%) patients died within 5 years, and the median OS was 50.6 months. A total of 98 (80.3%) patients suffered HCC relapse within 5 years, and the median DFS was 19.8 months.
The Kaplan-Meier curves for OS or DFS was significantly different between patient groups dichotomized by cut-off values for all CT texture parameters with filtration at at least single filter level (Table 2, Fig. 2). For SD and entropy, OS was significantly different without filtration or with filtration at each filter level (fine, medium and coarse). OS was different without filtration or with filtration (medium) for skewness. For mean, MPP and kurtosis, OS was not different without filtration between patient groups, but it was different with filtration (fine, fine and medium and coarse, and fine and medium, respectively). DFS was significantly different for skewness and kurtosis without filtration or with filtration (fine and medium, fine and coarse, respectively). DFS was different for mean, SD, entropy and MPP with filtration at single filter level (fine, coarse, coarse, and coarse, respectively).
Link here for full article at nature.com