Literature DB >> 31621883

MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors.

Ankush Bhatia1,2, Maxwell Birger3, Harini Veeraraghavan4, Hyemin Um4, Florent Tixier4, Anna Sophia McKenney3, Marina Cugliari2, Annalise Caviasco2, Angelica Bialczak2, Rachna Malani1, Jessica Flynn5, Zhigang Zhang5, T Jonathan Yang6, Bianca D Santomasso1, Alexander N Shoushtari2, Robert J Young2.   

Abstract

BACKGROUND: Melanoma brain metastases historically portend a dismal prognosis, but recent advances in immune checkpoint inhibitors (ICIs) have been associated with durable responses in some patients. There are no validated imaging biomarkers associated with outcomes in patients with melanoma brain metastases receiving ICIs. We hypothesized that radiomic analysis of magnetic resonance images (MRIs) could identify higher-order features associated with survival.
METHODS: Between 2010 and 2019, we retrospectively reviewed patients with melanoma brain metastases who received ICI. After volumes of interest were drawn, several texture and edge descriptors, including first-order, Haralick, Gabor, Sobel, and Laplacian of Gaussian (LoG) features were extracted. Progression was determined using Response Assessment in Neuro-Oncology Brain Metastases. Univariate Cox regression was performed for each radiomic feature with adjustment for multiple comparisons followed by Lasso regression and multivariate analysis.
RESULTS: Eighty-eight patients with 196 total brain metastases were identified. Median age was 63.5 years (range, 19-91 y). Ninety percent of patients had Eastern Cooperative Oncology Group performance status of 0 or 1 and 35% had elevated lactate dehydrogenase. Sixty-three patients (72%) received ipilimumab, 11 patients (13%) received programmed cell death protein 1 blockade, and 14 patients (16%) received nivolumab plus ipilimumab. Multiple features were associated with increased overall survival (OS), and LoG edge features best explained the variation in outcome (hazard ratio: 0.68, P = 0.001). In multivariate analysis, a similar trend with LoG was seen, but no longer significant with OS. Findings were confirmed in an independent cohort.
CONCLUSION: Higher-order MRI radiomic features in patients with melanoma brain metastases receiving ICI were associated with a trend toward improved OS.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  MRI radiomic features; brain metastases; imaging biomarkers; immune checkpoint inhibitors; intratumoral heterogeneity

Mesh:

Substances:

Year:  2019        PMID: 31621883      PMCID: PMC7145582          DOI: 10.1093/neuonc/noz141

Source DB:  PubMed          Journal:  Neuro Oncol        ISSN: 1522-8517            Impact factor:   12.300


  36 in total

1.  Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Authors:  Jose R Teruel; Mariann G Heldahl; Pål E Goa; Martin Pickles; Steinar Lundgren; Tone F Bathen; Peter Gibbs
Journal:  NMR Biomed       Date:  2014-05-20       Impact factor: 4.044

2.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.

Authors:  J G Daugman
Journal:  J Opt Soc Am A       Date:  1985-07       Impact factor: 2.129

3.  Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance.

Authors:  Carlo N De Cecco; Balaji Ganeshan; Maria Ciolina; Marco Rengo; Felix G Meinel; Daniela Musio; Francesca De Felice; Nicola Raffetto; Vincenzo Tombolini; Andrea Laghi
Journal:  Invest Radiol       Date:  2015-04       Impact factor: 6.016

4.  Demographics, prognosis, and therapy in 702 patients with brain metastases from malignant melanoma.

Authors:  J H Sampson; J H Carter; A H Friedman; H F Seigler
Journal:  J Neurosurg       Date:  1998-01       Impact factor: 5.115

5.  Determinants of outcome in melanoma patients with cerebral metastases.

Authors:  K M Fife; M H Colman; G N Stevens; I C Firth; D Moon; K F Shannon; R Harman; K Petersen-Schaefer; A C Zacest; M Besser; G W Milton; W H McCarthy; J F Thompson
Journal:  J Clin Oncol       Date:  2004-04-01       Impact factor: 44.544

6.  Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

Authors:  Leland S Hu; Shuluo Ning; Jennifer M Eschbacher; Leslie C Baxter; Nathan Gaw; Sara Ranjbar; Jonathan Plasencia; Amylou C Dueck; Sen Peng; Kris A Smith; Peter Nakaji; John P Karis; C Chad Quarles; Teresa Wu; Joseph C Loftus; Robert B Jenkins; Hugues Sicotte; Thomas M Kollmeyer; Brian P O'Neill; William Elmquist; Joseph M Hoxworth; David Frakes; Jann Sarkaria; Kristin R Swanson; Nhan L Tran; Jing Li; J Ross Mitchell
Journal:  Neuro Oncol       Date:  2016-08-08       Impact factor: 12.300

7.  Improved Risk-Adjusted Survival for Melanoma Brain Metastases in the Era of Checkpoint Blockade Immunotherapies: Results from a National Cohort.

Authors:  J Bryan Iorgulescu; Maya Harary; Cheryl K Zogg; Keith L Ligon; David A Reardon; F Stephen Hodi; Ayal A Aizer; Timothy R Smith
Journal:  Cancer Immunol Res       Date:  2018-07-12       Impact factor: 11.151

8.  Minkowski functionals: An MRI texture analysis tool for determination of the aggressiveness of breast cancer.

Authors:  Michael J Fox; Peter Gibbs; Martin D Pickles
Journal:  J Magn Reson Imaging       Date:  2015-10-10       Impact factor: 4.813

9.  Multiregion Whole-Exome Sequencing Uncovers the Genetic Evolution and Mutational Heterogeneity of Early-Stage Metastatic Melanoma.

Authors:  Katja Harbst; Martin Lauss; Helena Cirenajwis; Karolin Isaksson; Frida Rosengren; Therese Törngren; Anders Kvist; Maria C Johansson; Johan Vallon-Christersson; Bo Baldetorp; Åke Borg; Håkan Olsson; Christian Ingvar; Ana Carneiro; Göran Jönsson
Journal:  Cancer Res       Date:  2016-05-23       Impact factor: 12.701

10.  Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

Authors:  Leland S Hu; Shuluo Ning; Jennifer M Eschbacher; Nathan Gaw; Amylou C Dueck; Kris A Smith; Peter Nakaji; Jonathan Plasencia; Sara Ranjbar; Stephen J Price; Nhan Tran; Joseph Loftus; Robert Jenkins; Brian P O'Neill; William Elmquist; Leslie C Baxter; Fei Gao; David Frakes; John P Karis; Christine Zwart; Kristin R Swanson; Jann Sarkaria; Teresa Wu; J Ross Mitchell; Jing Li
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

View more
  22 in total

1.  Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features.

Authors:  Ruichuan Shi; Weixing Chen; Bowen Yang; Jinglei Qu; Yu Cheng; Zhitu Zhu; Yu Gao; Qian Wang; Yunpeng Liu; Zhi Li; Xiujuan Qu
Journal:  Am J Cancer Res       Date:  2020-12-01       Impact factor: 6.166

2.  Imaging biomarkers for brain metastases: more than meets the eye.

Authors:  Sanjay Aneja; Antonio Omuro
Journal:  Neuro Oncol       Date:  2019-12-17       Impact factor: 12.300

Review 3.  Machine Learning-Based Radiomics in Neuro-Oncology.

Authors:  Felix Ehret; David Kaul; Hans Clusmann; Daniel Delev; Julius M Kernbach
Journal:  Acta Neurochir Suppl       Date:  2022

4.  Prognostic value of neutrophil-lymphocyte ratio and lactate dehydrogenase in melanoma patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis.

Authors:  Yongchao Zhang; Bozhi Liu; Sergei Kotenko; Wei Li
Journal:  Medicine (Baltimore)       Date:  2022-08-12       Impact factor: 1.817

5.  Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study.

Authors:  Natally Horvat; Harini Veeraraghavan; Caio S R Nahas; David D B Bates; Felipe R Ferreira; Junting Zheng; Marinela Capanu; James L Fuqua; Maria Clara Fernandes; Ramon E Sosa; Vetri Sudar Jayaprakasam; Giovanni G Cerri; Sergio C Nahas; Iva Petkovska
Journal:  Abdom Radiol (NY)       Date:  2022-06-16

Review 6.  Radiomics in immuno-oncology.

Authors:  Z Bodalal; I Wamelink; S Trebeschi; R G H Beets-Tan
Journal:  Immunooncol Technol       Date:  2021-04-16

7.  Perilesional edema in brain metastases as predictive factor of response to systemic therapy in non-small cell lung cancer patients: a preliminary study.

Authors:  Montse Alemany; Marta Domènech; Andreas A Argyriou; Noelia Vilariño; Carles Majós; Pablo Naval-Baudin; Anna Lucas; Ramón Palmero; Marta Simó; Ernest Nadal; Jordi Bruna
Journal:  Ann Transl Med       Date:  2021-04

8.  Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon.

Authors:  Emilie Darrigues; Benjamin W Elberson; Annick De Loose; Madison P Lee; Ebonye Green; Ashley M Benton; Ladye G Sink; Hayden Scott; Murat Gokden; John D Day; Analiz Rodriguez
Journal:  Front Oncol       Date:  2021-04-26       Impact factor: 6.244

Review 9.  Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging.

Authors:  Ji Eun Park; Philipp Kickingereder; Ho Sung Kim
Journal:  Korean J Radiol       Date:  2020-07-27       Impact factor: 3.500

10.  Does the application of diffusion weighted imaging improve the prediction of survival in patients with resected brain metastases? A retrospective multicenter study.

Authors:  Rasheed Zakaria; Yin Jie Chen; David M Hughes; Sumei Wang; Sanjeev Chawla; Harish Poptani; Anna S Berghoff; Matthias Preusser; Michael D Jenkinson; Suyash Mohan
Journal:  Cancer Imaging       Date:  2020-02-07       Impact factor: 3.909

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.