Harvard Medical School - Dana-Farber Cancer Institute


For a complete overview of the publications of the CIBL please look at PUBMED

Publication Highlights

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P
In collaboration with Maastro Clinic the CIBL published a large Radiomics study in Nature Communications. With this publication two large datasets were shared (see Data page for more information).
Volumetric CT-based segmentation of NSCLC using 3D-Slicer
Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, Lewis JH, De Ruysscher D, Kikinis R, Lambin P, Aerts HJ
In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform.
Inconsistency in large pharmacogenomic studies
Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJ, Quackenbush J
In collaboration with the Haibe-Kains lab and Quackenbush lab, the CIBL published a study in Nature comparing two large pharmacogenomic datasets.
Enhancing Reproducibility in Cancer Drug Screening: How Do We Move Forward?
Hatzis C, Bedard PL, Birkbak NJ, Beck AH, Aerts HJ, Stern DF, Shi L, Clarke R, Quackenbush J, Haibe-Kains B
This publication suggests a path forward to establish better practices and standardizations of critical steps in pharmacogenomic assays.
Robust radiomics feature quantification using semiautomatic volumetric segmentation
Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, Lambin P, Aerts HJ
In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative Radiomics feature extraction.
Importance of collection in gene set enrichment analysis of drug response in cancer cell lines
Bateman AR, El-Hachem N, Beck AH, Aerts HJ, Haibe-Kains B
The publication assessed the influence of selected gene-sets for Gene set enrichment analysis (GSEA). The results show that GSEA findings vary significantly depending on the collection chosen for analysis.
Comparison and validation of genomic predictors for anticancer drug sensitivity
Papillon-Cavanagh S, De Jay N, Hachem N, Olsen C, Bontempi G, Aerts HJ, Quackenbush J, Haibe-Kains B
In this study the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel.
Predicting outcomes in radiation oncology–multifactorial decision support systems
Lambin P, van Stiphout RG, Starmans MH, Rios-Velazquez E, Nalbantov G, Aerts HJ, Roelofs E, van Elmpt W, Boutros PC, Granone P, Valentini V, Begg AC, De Ruysscher D, Dekker A
In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process.
Radiomics: extracting more information from medical images using advanced feature analysis.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ
In this review we introduced the field of radiomics by describing the methodology and the clinical need.
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