Harvard Medical School - Dana-Farber Cancer Institute


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

Publication Highlights

Defining the biological basis of radiomic phenotypes in lung cancer
Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, Leijenaar RT, Haibe-Kains B, Lambin P, Gillies R, Aerts HJ
In collaboration with Moffitt the CIBL published a large Radiomics study in eLIFE defining the biological basis of radiomic phenotypes.
Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer
Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, Ye Z, Makrigiorgos M, Fennessy F, Mak RH, Gillies R, Quackenbush J, Aerts HJWL
In large and independent datasets we evaluated the associations between somatic mutations and imaging phenotypes in lung cancer
Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.
Abbosh C, Birkbak NJ, Wilson GA, Jamal-Hanjani M, Constantin T, Salari R, Le Quesne J, Moore DA, Veeriah S, Rosenthal R, Marafioti T, Kirkizlar E, Watkins TBK, McGranahan N, Ward S, Martinson L, Riley J, Fraioli F, Al Bakir M, Grönroos E, Zambrana F, Endozo R, Bi WL, Fennessy FM, Sponer N, Johnson D, Laycock J, Shafi S, Czyzewska-Khan J, Rowan A, Chambers T, Matthews N, Turajlic S, Hiley C, Lee SM, Forster MD, Ahmad T, Falzon M, Borg E, Lawrence D, Hayward M, Kolvekar S, Panagiotopoulos N, Janes SM, Thakrar R, Ahmed A, Blackhall F, Summers Y, Hafez D, Naik A, Ganguly A, Kareht S, Shah R, Joseph L, Marie Quinn A, Crosbie PA, Naidu B, Middleton G, Langman G, Trotter S, Nicolson M, Remmen H, Kerr K, Chetty M, Gomersall L, Fennell DA, Nakas A, Rathinam S, Anand G, Khan S, Russell P, Ezhil V, Ismail B, Irvin-Sellers M, Prakash V, Lester JF, Kornaszewska M, Attanoos R, Adams H, Davies H, Oukrif D, Akarca AU, Hartley JA, Lowe HL, Lock S, Iles N, Bell H, Ngai Y, Elgar G, Szallasi Z, Schwarz RF, Herrero J, Stewart A, Quezada SA, Peggs KS, Van Loo P, Dive C, Lin CJ, Rabinowitz M, Aerts HJWL, Hackshaw A, Shaw JA, Zimmermann BG, Swanton C
In collaboration with the CRICK institute we investigate the importance of ctDNA in lung cancer
Quantitative Imaging Biomarkers for Risk Stratification of Patients with Recurrent Glioblastoma Treated with Bevacizumab
Grossmann P, Narayan V, Chang K, Rahman R, Abrey L, Reardon DA, Schwartz LH, Wen PY, Alexander BM, Huang R, Aerts HJWL
In this study we determined whether novel quantitative radiomic strategies on the basis of MRI have the potential to noninvasively stratify survival and progression in this patient population.
Tracking the Evolution of Non-Small-Cell Lung Cancer.
Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, Shafi S, Johnson DH, Mitter R, Rosenthal R, Salm M, Horswell S, Escudero M, Matthews N, Rowan A, Chambers T, Moore DA, Turajlic S, Xu H, Lee SM, Forster MD, Ahmad T, Hiley CT, Abbosh C, Falzon M, Borg E, Marafioti T, Lawrence D, Hayward M, Kolvekar S, Panagiotopoulos N, Janes SM, Thakrar R, Ahmed A, Blackhall F, Summers Y, Shah R, Joseph L, Quinn AM, Crosbie PA, Naidu B, Middleton G, Langman G, Trotter S, Nicolson M, Remmen H, Kerr K, Chetty M, Gomersall L, Fennell DA, Nakas A, Rathinam S, Anand G, Khan S, Russell P, Ezhil V, Ismail B, Irvin-Sellers M, Prakash V, Lester JF, Kornaszewska M, Attanoos R, Adams H, Davies H, Dentro S, Taniere P, O’Sullivan B, Lowe HL, Hartley JA, Iles N, Bell H, Ngai Y, Shaw JA, Herrero J, Szallasi Z, Schwarz RF, Stewart A, Quezada SA, Le Quesne J, Van Loo P, Dive C, Hackshaw A, Swanton C, TRACERx Consortium.
In collaboration with the CRICK institute we investigate the genomic heterogeneity in lung cancer
Computational Radiomics System to Decode the Radiographic Phenotype
van Griethuysen J.J.M., Fedorov A., Parmar C., Hosny A., Aucoin N., Narayan V., Beets-Tan R.G.H., Fillion-Robin J.C., Pieper S., Aerts HJWL
In this publication we introduce and apply the open source radiomics informatics platform PyRadiomics
The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review
Aerts HJ
This review outlines the promise of these novel technologies for precision medicine and the obstacles to clinical application.
Imaging biomarker roadmap for cancer studies
O’Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, Boellaard R, Bohndiek SE, Brady M, Brown G, Buckley DL, Chenevert TL, Clarke LP, Collette S, Cook GJ, deSouza NM, Dickson JC, Dive C, Evelhoch JL, Faivre-Finn C, Gallagher FA, Gilbert FJ, Gillies RJ, Goh V, Griffiths JR, Groves AM, Halligan S, Harris AL, Hawkes DJ, Hoekstra OS, Huang EP, Hutton BF, Jackson EF, Jayson GC, Jones A, Koh DM, Lacombe D, Lambin P, Lassau N, Leach MO, Lee TY, Leen EL, Lewis JS, Liu Y, Lythgoe MF, Manoharan P, Maxwell RJ, Miles KA, Morgan B, Morris S, Ng T, Padhani AR, Parker GJ, Partridge M, Pathak AP, Peet AC, Punwani S, Reynolds AR, Robinson SP, Shankar LK, Sharma RA, Soloviev D, Stroobants S, Sullivan DC, Taylor SA, Tofts PS, Tozer GM, van Herk M, Walker-Samuel S, Wason J, Williams KJ, Workman P, Yankeelov TE, Brindle KM, McShane LM, Jackson A, Waterton JC.
Large collaborative effort to define roadmaps for the clinical introduction of imaging biomarkers
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).
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.
Back to Top