Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study
Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association betwee...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-01-01
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| Series: | Human Vaccines & Immunotherapeutics |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21645515.2023.2172926 |
| _version_ | 1827806352541483008 |
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| author | Ernesto Rossi Luca Boldrini Maria Grazia Maratta Roberto Gatta Claudio Votta Giampaolo Tortora Giovanni Schinzari |
| author_facet | Ernesto Rossi Luca Boldrini Maria Grazia Maratta Roberto Gatta Claudio Votta Giampaolo Tortora Giovanni Schinzari |
| author_sort | Ernesto Rossi |
| collection | DOAJ |
| description | Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association between several radiomics features and response to immunotherapy in 53 patients treated with checkpoint inhibitors for advanced renal cell carcinoma. We found that the following features are associated with progression of disease as best tumor response: F_stat.range (p < .0004), F_stat.max (p < .0007), F_stat.var (p < .0016), F_stat.uniformity (p < .0020), F_stat.90thpercentile (p < .0050). Gross tumor volumes characterized by high values of F_stat.var and F_stat.max (greater than 60,000 and greater than 300, respectively) are most likely related to a high risk of progression. Further analyses are warranted to confirm these results. Radiomics, together with other potential predictive factors, such as gut microbiota, genetic features or circulating immune molecules, could allow a personalized treatment for patients with advanced renal cell carcinoma. |
| first_indexed | 2024-03-11T21:39:59Z |
| format | Article |
| id | doaj.art-a30090309951414494635a8b4eefd632 |
| institution | Directory Open Access Journal |
| issn | 2164-5515 2164-554X |
| language | English |
| last_indexed | 2024-03-11T21:39:59Z |
| publishDate | 2023-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Human Vaccines & Immunotherapeutics |
| spelling | doaj.art-a30090309951414494635a8b4eefd6322023-09-26T13:25:48ZengTaylor & Francis GroupHuman Vaccines & Immunotherapeutics2164-55152164-554X2023-01-0119110.1080/21645515.2023.21729262172926Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective studyErnesto Rossi0Luca Boldrini1Maria Grazia Maratta2Roberto Gatta3Claudio Votta4Giampaolo Tortora5Giovanni Schinzari6Fondazione Policlinico Universitario Agostino Gemelli IRCCSFondazione Policlinico Universitario Agostino Gemelli IRCCSFondazione Policlinico Universitario Agostino Gemelli IRCCSUniversitá degli Studi di BresciaFondazione Policlinico Universitario Agostino Gemelli IRCCSFondazione Policlinico Universitario Agostino Gemelli IRCCSFondazione Policlinico Universitario Agostino Gemelli IRCCSImmunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association between several radiomics features and response to immunotherapy in 53 patients treated with checkpoint inhibitors for advanced renal cell carcinoma. We found that the following features are associated with progression of disease as best tumor response: F_stat.range (p < .0004), F_stat.max (p < .0007), F_stat.var (p < .0016), F_stat.uniformity (p < .0020), F_stat.90thpercentile (p < .0050). Gross tumor volumes characterized by high values of F_stat.var and F_stat.max (greater than 60,000 and greater than 300, respectively) are most likely related to a high risk of progression. Further analyses are warranted to confirm these results. Radiomics, together with other potential predictive factors, such as gut microbiota, genetic features or circulating immune molecules, could allow a personalized treatment for patients with advanced renal cell carcinoma.http://dx.doi.org/10.1080/21645515.2023.2172926radiomicsfeaturesrenal cell carcinomaimmunotherapyanti-pd-1anti-ctla4nivolumabipilimumabpembrolizumabpredictive factor |
| spellingShingle | Ernesto Rossi Luca Boldrini Maria Grazia Maratta Roberto Gatta Claudio Votta Giampaolo Tortora Giovanni Schinzari Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study Human Vaccines & Immunotherapeutics radiomics features renal cell carcinoma immunotherapy anti-pd-1 anti-ctla4 nivolumab ipilimumab pembrolizumab predictive factor |
| title | Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study |
| title_full | Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study |
| title_fullStr | Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study |
| title_full_unstemmed | Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study |
| title_short | Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study |
| title_sort | radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma a retrospective study |
| topic | radiomics features renal cell carcinoma immunotherapy anti-pd-1 anti-ctla4 nivolumab ipilimumab pembrolizumab predictive factor |
| url | http://dx.doi.org/10.1080/21645515.2023.2172926 |
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