Literature DB >> 33497314

A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors.

Marta Ligero1, Alonso Garcia-Ruiz1, Cristina Viaplana1, Guillermo Villacampa1, Maria V Raciti1, Jaid Landa1, Ignacio Matos1, Juan Martin-Liberal1, Maria Ochoa-de-Olza1, Cinta Hierro1, Joaquin Mateo1, Macarena Gonzalez1, Rafael Morales-Barrera1, Cristina Suarez1, Jordi Rodon1, Elena Elez1, Irene Braña1, Eva Muñoz-Couselo1, Ana Oaknin1, Roberta Fasani1, Paolo Nuciforo1, Debora Gil1, Carlota Rubio-Perez1, Joan Seoane1, Enriqueta Felip1, Manuel Escobar1, Josep Tabernero1, Joan Carles1, Rodrigo Dienstmann1, Elena Garralda1, Raquel Perez-Lopez1.   

Abstract

Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

Entities:  

Year:  2021        PMID: 33497314     DOI: 10.1148/radiol.2021200928

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  12 in total

Review 1.  Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study.

Authors:  Syafiq Ramlee; David Hulse; Kinga Bernatowicz; Raquel Pérez-López; Evis Sala; Luigi Aloj
Journal:  Cancers (Basel)       Date:  2022-07-27       Impact factor: 6.575

2.  Response to: Correspondence on 'Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab' by Zheng et al.

Authors:  Vidya Sankar Viswanathan; Mohammadhadi Khorrami; Khalid Jazieh; Pingfu Fu; Nathan Pennell; Anant Madabhushi
Journal:  J Immunother Cancer       Date:  2022-05       Impact factor: 12.469

3.  Imaging Biomarkers to Assess Response to Immune Checkpoint Inhibitors in Solid Tumors to Tailor Therapy.

Authors:  Ronald M Summers
Journal:  Radiology       Date:  2021-01-26       Impact factor: 11.105

4.  Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.

Authors:  Hwan-Ho Cho; Ho Yun Lee; Eunjin Kim; Geewon Lee; Jonghoon Kim; Junmo Kwon; Hyunjin Park
Journal:  Commun Biol       Date:  2021-11-12

5.  A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

Authors:  Andreas Stefan Brendlin; Felix Peisen; Haidara Almansour; Saif Afat; Thomas Eigentler; Teresa Amaral; Sebastian Faby; Adria Font Calvarons; Konstantin Nikolaou; Ahmed E Othman
Journal:  J Immunother Cancer       Date:  2021-11       Impact factor: 13.751

6.  Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma.

Authors:  Konstantin Hellwig; Stephan Ellmann; Markus Eckstein; Marco Wiesmueller; Sandra Rutzner; Sabine Semrau; Benjamin Frey; Udo S Gaipl; Antoniu Oreste Gostian; Arndt Hartmann; Heinrich Iro; Rainer Fietkau; Michael Uder; Markus Hecht; Tobias Bäuerle
Journal:  Front Oncol       Date:  2021-10-21       Impact factor: 6.244

7.  Robust imaging habitat computation using voxel-wise radiomics features.

Authors:  Kinga Bernatowicz; Francesco Grussu; Marta Ligero; Alonso Garcia; Eric Delgado; Raquel Perez-Lopez
Journal:  Sci Rep       Date:  2021-10-11       Impact factor: 4.379

8.  Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study.

Authors:  Yi-Yang Liu; Huan Zhang; Lan Wang; Shu-Shen Lin; Hao Lu; He-Jun Liang; Pan Liang; Jun Li; Pei-Jie Lv; Jian-Bo Gao
Journal:  Front Oncol       Date:  2021-09-15       Impact factor: 6.244

Review 9.  Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

Authors:  Xingping Zhang; Yanchun Zhang; Guijuan Zhang; Xingting Qiu; Wenjun Tan; Xiaoxia Yin; Liefa Liao
Journal:  Front Oncol       Date:  2022-02-17       Impact factor: 6.244

10.  Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study.

Authors:  Yuyang Tong; Peixuan Sun; Juanjuan Yong; Hongbo Zhang; Yunxia Huang; Yi Guo; Jinhua Yu; Shichong Zhou; Yulong Wang; Yu Wang; Qinghai Ji; Yuanyuan Wang; Cai Chang
Journal:  Front Oncol       Date:  2021-06-29       Impact factor: 6.244

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