Haralick’s texture features for the prediction of response to therapy in colorectal cancer: a preliminary study

2017 ◽  
Vol 123 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Damiano Caruso ◽  
Marta Zerunian ◽  
Maria Ciolina ◽  
Domenico de Santis ◽  
Marco Rengo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Texture Features ◽  
Response To Therapy ◽  
Prediction Of Response ◽  
Preliminary Study

Radiomics features on CT scans to predict response to HER2-targeted therapy of hepatic metastases from colorectal cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15086-e15086
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Arianna Defeudis ◽  
Giovanni Cappello ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Texture Analysis ◽  
Hepatic Metastases ◽  
Texture Features ◽  
Response To Therapy ◽  
Ct Scans ◽  
Imaging Biomarkers ◽  
Test Set ◽  
Extended Analysis ◽  
Ct Texture Analysis

e15086 Background: Metastatic Colorectal cancer (mCRC) is the 2nd cause of cancer death worldwide. Repeated cycles of therapies, combined with surgery in oligo-metastatic cases, are the therapeutic standard in mCRC. However, this strategy is seldom resolutive. Different lesions in in the same patient could have different responses to systemic therapy. Recently, CT texture analysis (CTTA) had been shown to potentially provide with prognostic and predictive markers, overcoming the limitations of biopsy sampling in defining tumor heterogeneity. The aim of this study is to use CT texture analysis (CTTA) to identify imaging biomarkers of HER2+ mCRC able to predict lesion response to therapy. Methods: The dataset is composed of 39 extended RAS wild type patients with amplified HER2 mCRC enrolled in the HERACLES trial (NCT03225937) that received either a lapatinib+trastuzumab treatment (n = 23) or a pertuzumab+ trastuzumab-emtansine treatment (n = 16). All patients underwent CT examination every 8 weeks, until disease progression. All mCRC on baseline CT were semi-automatically segmented and quantitative features extracted: size, mean, percentiles, 28 texture features. A logistic regression model was created using: (i) the whole dataset of mCRC as training and test set and (ii) 100 randomly generated training sets (with 70% of responder (R+) mCRC and an equal number of non-responder (R-) mCRC), and 100 test sets including the remaining mCRC. A mCRC was classified as R+ if size decreased (-10%) or was stable (±10%); as R- if size increased (+10%), during subsequent CT scans. Results: A total of 199 metastases were included (75R+ and 124R-). The training set was composed of 53R+ and 53R- mCRC and the test set of 22R+ and 71R- mCRC. Using the whole dataset, the model reached an AUC = 0.82 (sensitivity = 84%, specificity = 70%), while it reached a mean AUC of 0.70 (sensitivity = 68%, specificity = 67%) within the 100 repetitions. Conclusions: CTTA might help in stratifying different behaviors of mCRC, opening the way for lesion-specific therapies, with conceivable cost and life savings. Further extended analysis is needed to better characterize and validate predictive value of these biomarkers.

scholarly journals Biomarker-Based Prediction of Response to Therapy for Colorectal Cancer

2010 ◽  
Vol 134 (3) ◽  
pp. 478-490 ◽  
Author(s):  
Jeffrey S. Ross ◽  
Jorge Torres-Mora ◽  
Nikhil Wagle ◽  
Timothy A. Jennings ◽  
David M. Jones
Keyword(s):  
Colorectal Cancer ◽  
Response To Therapy ◽  
Prediction Of Response

scholarly journals YY1 Silencing Induces 5-Fluorouracil-Resistance and BCL2L15 Downregulation in Colorectal Cancer Cells: Diagnostic and Prognostic Relevance

2021 ◽  
Vol 22 (16) ◽  
pp. 8481
Author(s):  
Silvia Vivarelli ◽  
Luca Falzone ◽  
Saverio Candido ◽  
Benjamin Bonavida ◽  
Massimo Libra
Keyword(s):  
Colorectal Cancer ◽  
Response To Therapy ◽  
Advanced Stage ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Expression Levels ◽  
Single Cell Sequencing ◽  
Yin Yang 1 ◽  
Anti Cancer ◽  
Colorectal Cancer Cells

Colorectal cancer (CRC) is characterized by genetic heterogeneity and is often diagnosed at an advanced stage. Therefore, there is a need to identify novel predictive markers. Yin Yang 1 (YY1) is a transcription factor playing a dual role in cancer. The present study aimed to investigate whether YY1 expression levels influence CRC cell response to therapy and to identify the transcriptional targets involved. The diagnostic and prognostic values of YY1 and the identified factor(s) in CRC patients were also explored. Silencing of YY1 increased the resistance to 5-Fluorouracil-induced cytotoxicity in two out of four CRC cells with different genotypes. BCL2L15/Bfk pro-apoptotic factor was found selectively expressed in the responder CRC cells and downregulated upon YY1 knockdown. CRC dataset analyses corroborated a tumor-suppressive role for both YY1 and BCL2L15 whose expressions were inversely correlated with aggressiveness. CRC single-cell sequencing dataset analyses demonstrated higher co-expression levels of both YY1 and BCL2L15 within defined tumor cell clusters. Finally, elevated levels of YY1 and BCL2L15 in CRC patients were associated with larger relapse-free survival. Given their observed anti-cancer role, we propose YY1 and BCL2L15 as candidate diagnostic and prognostic CRC biomarkers.

scholarly journals Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Emanuela Paladini ◽  
Edoardo Vantaggiato ◽  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Texture Features ◽  
Tissue Type ◽  
Cancer Tissue ◽  
Learning Methods ◽  
Feature Based ◽  
Type Classification ◽  
Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

scholarly journals Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 956
Author(s):  
Marcello Andrea Tipaldi ◽  
Edoardo Ronconi ◽  
Elena Lucertini ◽  
Miltiadis Krokidis ◽  
Marta Zerunian ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Clinical Data ◽  
Transarterial Chemoembolization ◽  
Prognostic Value ◽  
Texture Features ◽  
Response To Therapy ◽  
Survival Prediction ◽  
Ct Examination ◽  
Drug Eluting ◽  
Pre Treatment

(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 (p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03–2.35) using texture features, of 1.7 (95% CI: 1.54–1.9) using clinical data and of 4.61 (95% CI: 4.24–5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.

Identification of high-risk Dukes B colorectal cancer by microRNA expression profiling: a preliminary study

2015 ◽  
Vol 17 (7) ◽  
pp. 578-588 ◽  
Author(s):  
M. I. Aslam ◽  
J. Venkatesh ◽  
J. S. Jameson ◽  
K. West ◽  
J. H. Pringle ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Expression Profiling ◽  
Microrna Expression ◽  
Preliminary Study

scholarly journals Genitourinary Tumors: Update on Molecular Biomarkers for Diagnosis, Prognosis and Prediction of Response to Therapy

2019 ◽  
Vol 20 (4) ◽  
pp. 305-312 ◽  
Author(s):  
Alessia Cimadamore ◽  
Marina Scarpelli ◽  
Matteo Santoni ◽  
Francesco Massari ◽  
Francesca Tartari ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Renal Cell Carcinoma ◽  
Germ Cell ◽  
Renal Cell ◽  
Germ Cell Tumors ◽  
Response To Therapy ◽  
Testicular Germ Cell Tumors ◽  
Prediction Of Response ◽  
Testicular Germ Cell ◽  
Prognosis And Prediction

Background:Research of biomarkers in genitourinary tumors goes along with the development of complex emerging techniques ranging from next generation sequencing platforms, applied to archival pathology specimens, cytological samples, liquid biopsies, and to patient-derived tumor models.Methods:This contribution is an update on molecular biomarkers for diagnosis, prognosis and prediction of response to therapy in genitourinary tumors. The following major topics are dealt with: Immunological biomarkers, including the microbiome, and their potential role and caveats in renal cell carcinoma, bladder and prostate cancers and testicular germ cell tumors; Tissue biomarkers for imaging and therapy, with emphasis on Prostate-specific membrane antigen in prostate cancer; Liquid biomarkers in prostate cancer, including circulating tumor cell isolation and characterization in renal cell carcinoma, bladder cancer with emphasis on biomarkers detectable in the urine and testicular germ cell tumors; and Biomarkers and economic sustainability.Conclusion:The identification of effective biomarkers has become a major focus in cancer research, mainly due to the necessity of selecting potentially responsive patients in order to improve their outcomes, as well as to reduce the toxicity and costs related to ineffective treatments.

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