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Feedback PLC – ICENI Centre & ISLCRS study to investigate whether textural features of rectal cancer on Magnetic Resonance can predict long term survival
Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long term survival in locally advanced rectal cancer patients.
Omer Jalil, Asim Afaq, Balaji Ganeshan, Uday Patel, Raymond Endozo, Darren Boone, Ashley Groves, David Humber, Tan Arulampalam
ICENI Centre
Colchester University Hospital
Colchester
CO4 5JL
Introduction:
Heterogeneity is a well-known feature of malignancy and is associated with adverse outcomes. Tumour heterogeneity can be quantified on imaging using textural analysis (TA), which assesses the distribution of pixel grey-level intensity, coarseness and regularity and have shown to predict survival outcomes in a number of oncological applications. The main objective of this study was to investigate whether textural features of rectal cancer on MR can predict long term survival.
Methodology:
Patients with primary non-metastatic locally advanced rectal adenocarcinoma treated with long course chemoradiotherapy (LCRT) with curative intent from 01/2006 to 06/2011 in our institution were included. TA of T2-weighted pre and post LCRT MR images was undertaken using TexRAD, a proprietary software algorithm (TexRAD Ltd www.texrad.com, part of Feedback Plc). Tumour region of interest (ROI) delineating the largest cross-sectional area underwent MRTA comprising an image filtration-histogram technique. Filtration step extracted features of different sizes (fine, medium, coarse – texture scales) followed by quantification of statistical features (mean intensity, standard-deviation, entropy, skewness, kurtosis and mean of positive pixels – MPP) using histogram analysis. Univariate Kaplan Meier analysis was used to assess the ability of the biomarkers (textures and other clinically employed radiological and histological features) to predict survival. Cox-multiple regression analysis determined which significant univariate markers were independent predictors of survival.
Results:
For overall survival (OS), pre-treatment texture features (MPP at fine-texture, p=0.008, Mean at medium-texture, p=0.03) were significant univaraite markers of OS. Using multi-variate analysis, these pre-treatment textures (MPP at fine-texture, HR: 6.5, 95% CI: 1.9 – 22.0, p=0.002, Mean at medium-texture, HR: 5.6, 95% CI: 1.4 – 21.7, p=0.013) and post-treatment EMVI positive status (HR: 5.2, 95% CI: 1.6 – 16.8, p=0.006) were the only independent predictors of OS.
For disease-free survival (DFS), pre-treatment texture (Mean at medium-texture, p=0.007) and post-treatment texture (Kurtosis at medium-texture, p=0.009) were significant univaraite markers of DFS. Using multi-variate analysis, the post-treatment textures (Kurtosis at medium-texture, HR: 3.5, 95% CI: 1.3 – 9.5, p=0.013) and pre-treatment CRM involvement (HR: 5.2, 95% CI: 1.5 – 18.1, p=0.010) were the only independent predictors of DFS
Conclusion:
Non-invasive imaging features derived during the filtration histogram method of MR Textural analysis can predict patients with poorer prognosis before undergoing surgery, and could lead to selection of patients for more intensive treatment before undergoing surgery.
Table1- Significant features on univariate and multivariate analysis-OS
| OS: Significant pre-treatment texture parameters | Filter value | Univariate analysis P value | Multivariate analysis p value |
| Mean | 3 | 0.03 | .013 |
| MPP | 2 | 0.008 | .002 |
| 3 | 0.029 | ||
| 4 | 0.019 | ||
| OS: Significant post-treatment texture parameters | |||
| Skewness | 2 | .034 | |
| OS: Significant Clinical Variables | |||
| Pre treatment | |||
| EMVI status | .017 | ||
| CRM status | .036 | ||
| Post treatment | |||
| EMVI status | .002 | .006 | |
| CRM status | .027 | ||
| TRG status | .002 | ||
| ypCRM involvement | .007 |
Table 2: Significant features on univariate and multivariate analysis- DFS
| DFS: Significant pre-treatment texture parameters | Filter Value | Univariate Analysis P value | Multivariate Analysis P value |
| Mean | 2 | 0.031 | |
| 3 | 0.007 | ||
| 4 | 0.027 | ||
| 6 | 0.043 | ||
| MPP | 2 | 0.022 | |
| 3 | 0.045 | ||
| 4 | 0.022 | ||
| 5 | 0.047 | ||
| 6 | 0.047 | ||
| Skewness | 2 | 0.044 | |
| DFS: Significant post-treatment texture parameters | |||
| MPP | 2 | 0.032 | |
| Skewness | 2 | 0.034 | |
| Kurtosis | 3 | 0.042 | |
| 4 | 0.009 | .013 | |
| DFS- Significant Clinical Variables | |||
| Pre treatment | |||
| CRM status | .006 | .010 | |
| EMVI status | .017 | ||
| CRM status | .019 | ||
| TRG status | .022 | ||
| PCR (ypT0N0M0) | .035 | ||
Strategic alliance between Quibim and Feedback Plc to improve prostate biopsy
From the Spanish medical website Tu hospital investiga para ti
The Spin-Off of the IIS La Fe, Quibim has established a strategic alliance with UK company Feedback Plc to develop Prostate Checker, a tool that will improve the diagnosis of prostate cancer.
Feedback Plc acquired last year TexRAD company dedicated to the analysis of tumor from medical images textures, and began negotiating with Quibim in June this year in order to integrate the analysis modules of cell broadcasting and angiogenesis tumor that develops the Valencian company.
The information provided by Quibim be integrated with the texture analysis of Feedback in a single tool, under the name of Prostate Checker. These imaging biomarkers can be obtained from the advanced processing of MRI and is now considered the revolution He is undergoing radiology to become an indispensable tool in the framework of Medicine Precision “.
Quibim is an innovative company, founded in 2012, especially dedicated to medical image processing and extraction of imaging biomarkers in the radiological workflow.
More information Prostatechecker
More information Quibim
Feedback Plc – Presentation of Study Results
Feedback Plc – Presentation of Study Results
On July 20th 2015, Feedback plc announced the successful completion of a large evaluation of TexRAD CT texture analysis (CTTA) as a pre-therapy imaging biomarker in 241 metastatic renal (kidney) cell cancer (m-RCC) patients treated with anti-angiogenic therapy (AAT – drugs that block cancer blood-vessel growth).
The research study was led by Dr. Andrew Smith (Associate Professor in Radiology and Director of Radiology Research) along with colleagues from the University of Mississippi Medical Center, Jackson, Mississippi, USA (UMMC).
Specifically the results from this large study demonstrated that TexRAD texture analysis on conventional CT imaging acquired before m-RCC patients underwent AAT was a very strong predictor of overall survival at 2 years. Dr. Smith and colleagues further demonstrated that in a multivariate model comprising of known clinical biomarkers in this cancer population, TexRAD CTTA additionally demonstrated to be a strongly independent predictor of overall survival. The authors have developed a “new prognostic risk index” comprising of TexRAD CTTA and other known clinical factors which has demonstrated superior accuracy in predicting patient survival and classified patients into low, medium and high-risk groups with strong statistical significance (p<0.0001). Dr. Smith and colleagues also concluded that pre-therapy TexRAD CTTA is a significant prognostic marker in patients with m-RCC treated with AAT.
On October 7th-11th, 2015 Dr Andrew Smith presented the data from this large patient study (n=241) on the use of TexRAD texture analysis on CT scans in predicting survival in secondary kidney cancer patients treated with anti-vascular drugs.
The presentation at the Society of Computed Body Tomography and Magnetic Resonance (SCBT-MR) Conference was awarded the Best Junior Researcher Presentation. Below are some slides from the presentation.
