Literature DB >> 32253232

Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition.

Matthias Guckenberger1, Mitchell P Levesque2, Lucas Basler1, Hubert S Gabryś1, Sabrina A Hogan3, Matea Pavic1, Marta Bogowicz1, Diem Vuong1, Stephanie Tanadini-Lang1, Robert Förster1, Ken Kudura4, Martin W Huellner4, Reinhard Dummer3.   

Abstract

PURPOSE: We assessed the predictive potential of positron emission tomography (PET)/CT-based radiomics, lesion volume, and routine blood markers for early differentiation of pseudoprogression from true progression at 3 months. EXPERIMENTAL
DESIGN: 112 patients with metastatic melanoma treated with immune checkpoint inhibition were included in our study. Median follow-up duration was 22 months. 716 metastases were segmented individually on CT and 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET imaging at three timepoints: baseline (TP0), 3 months (TP1), and 6 months (TP2). Response was defined on a lesion-individual level (RECIST 1.1) and retrospectively correlated with FDG-PET/CT radiomic features and the blood markers LDH/S100. Seven multivariate prediction model classes were generated.
RESULTS: Two-year (median) overall survival, progression-free survival, and immune progression-free survival were 69% (not reached), 24% (6 months), and 42% (16 months), respectively. At 3 months, 106 (16%) lesions had progressed, of which 30 (5%) were identified as pseudoprogression at 6 months. Patients with pseudoprogressive lesions and without true progressive lesions had a similar outcome to responding patients and a significantly better 2-year overall survival of 100% (30 months), compared with 15% (10 months) in patients with true progressions/without pseudoprogression (P = 0.002). Patients with mixed progressive/pseudoprogressive lesions were in between at 53% (25 months). The blood prediction model (LDH+S100) achieved an AUC = 0.71. Higher LDH/S100 values indicated a low chance of pseudoprogression. Volume-based models: AUC = 0.72 (TP1) and AUC = 0.80 (delta-volume between TP0/TP1). Radiomics models (including/excluding volume-related features): AUC = 0.79/0.78. Combined blood/volume model: AUC = 0.79. Combined blood/radiomics model (including volume-related features): AUC = 0.78. The combined blood/radiomics model (excluding volume-related features) performed best: AUC = 0.82.
CONCLUSIONS: Noninvasive PET/CT-based radiomics, especially in combination with blood parameters, are promising biomarkers for early differentiation of pseudoprogression, potentially avoiding added toxicity or delayed treatment switch. ©2020 American Association for Cancer Research.

Entities:  

Year:  2020        PMID: 32253232     DOI: 10.1158/1078-0432.CCR-20-0020

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  22 in total

1.  Delta radiomics: a systematic review.

Authors:  Valerio Nardone; Alfonso Reginelli; Roberta Grassi; Luca Boldrini; Giovanna Vacca; Emma D'Ippolito; Salvatore Annunziata; Alessandra Farchione; Maria Paola Belfiore; Isacco Desideri; Salvatore Cappabianca
Journal:  Radiol Med       Date:  2021-12-04       Impact factor: 3.469

2.  Correction: Kudura et al. Prediction of Early Response to Immune Checkpoint Inhibition Using FDG-PET/CT in Melanoma Patients. Cancers 2021, 13, 3830.

Authors:  Ken Kudura; Florentia Dimitriou; Lucas Basler; Robert Förster; Daniela Mihic-Probst; Tim Kutzker; Reinhard Dummer; Joanna Mangana; Irene A Burger; Michael C Kreissl
Journal:  Cancers (Basel)       Date:  2022-07-04       Impact factor: 6.575

3.  Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.

Authors:  Leonardo Rundo; Lucian Beer; Lorena Escudero Sanchez; Mireia Crispin-Ortuzar; Marika Reinius; Cathal McCague; Hilal Sahin; Vlad Bura; Roxana Pintican; Marta Zerunian; Stephan Ursprung; Iris Allajbeu; Helen Addley; Paula Martin-Gonzalez; Thomas Buddenkotte; Naveena Singh; Anju Sahdev; Ionut-Gabriel Funingana; Mercedes Jimenez-Linan; Florian Markowetz; James D Brenton; Evis Sala; Ramona Woitek
Journal:  Front Oncol       Date:  2022-06-16       Impact factor: 5.738

Review 4.  Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.

Authors:  David Morland; Elizabeth Katherine Anna Triumbari; Luca Boldrini; Roberto Gatta; Daniele Pizzuto; Salvatore Annunziata
Journal:  Diagnostics (Basel)       Date:  2022-05-27

5.  18FDG PET Assessment of Therapeutic Response in Patients with Advanced or Metastatic Melanoma Treated with First-Line Immune Checkpoint Inhibitors.

Authors:  Alexia Rivas; Julie Delyon; Antoine Martineau; Estelle Blanc; Clara Allayous; Laetitia Da Meda; Pascal Merlet; Céleste Lebbé; Barouyr Baroudjian; Laetitia Vercellino
Journal:  Cancers (Basel)       Date:  2022-06-29       Impact factor: 6.575

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

Review 7.  Prognostic and Predictive Biomarkers in Stage III Melanoma: Current Insights and Clinical Implications.

Authors:  Luca Tonella; Valentina Pala; Renata Ponti; Marco Rubatto; Giuseppe Gallo; Luca Mastorino; Gianluca Avallone; Martina Merli; Andrea Agostini; Paolo Fava; Luca Bertero; Rebecca Senetta; Simona Osella-Abate; Simone Ribero; Maria Teresa Fierro; Pietro Quaglino
Journal:  Int J Mol Sci       Date:  2021-04-27       Impact factor: 5.923

Review 8.  Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

Authors:  Cyra Y Kang; Samantha E Duarte; Hye Sung Kim; Eugene Kim; Jonghanne Park; Alice Daeun Lee; Yeseul Kim; Leeseul Kim; Sukjoo Cho; Yoojin Oh; Gahyun Gim; Inae Park; Dongyup Lee; Mohamed Abazeed; Yury S Velichko; Young Kwang Chae
Journal:  Oncologist       Date:  2022-06-08       Impact factor: 5.837

Review 9.  Emerging Technologies for Non-invasive Monitoring of Treatment Response to Immunotherapy for Brain Tumors.

Authors:  Dimitrios Mathios; Siddhartha Srivastava; Timothy Kim; Chetan Bettegowda; Michael Lim
Journal:  Neuromolecular Med       Date:  2021-07-23       Impact factor: 3.843

10.  Malignancy Rate of Indeterminate Findings on FDG-PET/CT in Cutaneous Melanoma Patients.

Authors:  Ken Kudura; Florentia Dimitriou; Daniela Mihic-Probst; Urs J Muehlematter; Tim Kutzker; Lucas Basler; Robert Förster; Reinhard Dummer; Joanna Mangana; Lars Husmann; Irene A Burger; Michael Christoph Kreissl
Journal:  Diagnostics (Basel)       Date:  2021-05-15
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