Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds—urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics tha...
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| Veröffentlicht in: | Annals of oncology 2019-06, Vol.30 (6), p.998-1004 |
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| creator | Trebeschi, S. Drago, S.G. Birkbak, N.J. Kurilova, I. Cǎlin, A.M. Delli Pizzi, A. Lalezari, F. Lambregts, D.M.J. Rohaan, M.W. Parmar, C. Rozeman, E.A. Hartemink, K.J. Swanton, C. Haanen, J B A G Blank, C.U. Smit, E.F. Beets-Tan, R.G.H. Aerts, H.J.W.L |
| description | Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds—urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.
In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.
The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P |
| doi_str_mv | 10.1093/annonc/mdz108 |
| format | Article |
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In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.
The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P<0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P=0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P<0.001), resulting in a 1-year survival difference of 24% (P=0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.
These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.</description><identifier>ISSN: 0923-7534</identifier><identifier>EISSN: 1569-8041</identifier><identifier>DOI: 10.1093/annonc/mdz108</identifier><identifier>PMID: 30895304</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Algorithms ; Antineoplastic Combined Chemotherapy Protocols - therapeutic use ; Artificial Intelligence ; Carcinoma, Non-Small-Cell Lung - diagnostic imaging ; Carcinoma, Non-Small-Cell Lung - drug therapy ; Carcinoma, Non-Small-Cell Lung - immunology ; Carcinoma, Non-Small-Cell Lung - pathology ; Editor's Choice ; Follow-Up Studies ; Humans ; immunotherapy ; Immunotherapy - methods ; Lung Neoplasms - diagnostic imaging ; Lung Neoplasms - drug therapy ; Lung Neoplasms - immunology ; Lung Neoplasms - pathology ; Machine Learning ; medical imaging ; Melanoma - diagnostic imaging ; Melanoma - drug therapy ; Melanoma - immunology ; Melanoma - pathology ; Original articles ; Predictive Value of Tests ; Prognosis ; Programmed Cell Death 1 Receptor - antagonists & inhibitors ; Programmed Cell Death 1 Receptor - immunology ; radiomics ; response prediction ; Survival Rate ; Tomography, X-Ray Computed - methods</subject><ispartof>Annals of oncology, 2019-06, Vol.30 (6), p.998-1004</ispartof><rights>2019 THE AUTHORS</rights><rights>The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology.</rights><rights>The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology. 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c501t-4842df991f42a5ee65c73c8ecfff2ec2561dc66cbe1972605f21cffb8bf3f26a3</citedby><cites>FETCH-LOGICAL-c501t-4842df991f42a5ee65c73c8ecfff2ec2561dc66cbe1972605f21cffb8bf3f26a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30895304$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Trebeschi, S.</creatorcontrib><creatorcontrib>Drago, S.G.</creatorcontrib><creatorcontrib>Birkbak, N.J.</creatorcontrib><creatorcontrib>Kurilova, I.</creatorcontrib><creatorcontrib>Cǎlin, A.M.</creatorcontrib><creatorcontrib>Delli Pizzi, A.</creatorcontrib><creatorcontrib>Lalezari, F.</creatorcontrib><creatorcontrib>Lambregts, D.M.J.</creatorcontrib><creatorcontrib>Rohaan, M.W.</creatorcontrib><creatorcontrib>Parmar, C.</creatorcontrib><creatorcontrib>Rozeman, E.A.</creatorcontrib><creatorcontrib>Hartemink, K.J.</creatorcontrib><creatorcontrib>Swanton, C.</creatorcontrib><creatorcontrib>Haanen, J B A G</creatorcontrib><creatorcontrib>Blank, C.U.</creatorcontrib><creatorcontrib>Smit, E.F.</creatorcontrib><creatorcontrib>Beets-Tan, R.G.H.</creatorcontrib><creatorcontrib>Aerts, H.J.W.L</creatorcontrib><title>Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers</title><title>Annals of oncology</title><addtitle>Ann Oncol</addtitle><description>Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds—urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.
In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.
The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P<0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P=0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P<0.001), resulting in a 1-year survival difference of 24% (P=0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.
These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.</description><subject>Algorithms</subject><subject>Antineoplastic Combined Chemotherapy Protocols - therapeutic use</subject><subject>Artificial Intelligence</subject><subject>Carcinoma, Non-Small-Cell Lung - diagnostic imaging</subject><subject>Carcinoma, Non-Small-Cell Lung - drug therapy</subject><subject>Carcinoma, Non-Small-Cell Lung - immunology</subject><subject>Carcinoma, Non-Small-Cell Lung - pathology</subject><subject>Editor's Choice</subject><subject>Follow-Up Studies</subject><subject>Humans</subject><subject>immunotherapy</subject><subject>Immunotherapy - methods</subject><subject>Lung Neoplasms - diagnostic imaging</subject><subject>Lung Neoplasms - drug therapy</subject><subject>Lung Neoplasms - immunology</subject><subject>Lung Neoplasms - pathology</subject><subject>Machine Learning</subject><subject>medical imaging</subject><subject>Melanoma - diagnostic imaging</subject><subject>Melanoma - drug therapy</subject><subject>Melanoma - immunology</subject><subject>Melanoma - pathology</subject><subject>Original articles</subject><subject>Predictive Value of Tests</subject><subject>Prognosis</subject><subject>Programmed Cell Death 1 Receptor - antagonists & inhibitors</subject><subject>Programmed Cell Death 1 Receptor - immunology</subject><subject>radiomics</subject><subject>response prediction</subject><subject>Survival Rate</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>0923-7534</issn><issn>1569-8041</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kc1P3DAUxK0KVBbaY69VjlwC_oi98QWpQuVDQgIkeracl-fF7cZe7GQl-tdjlC2iB07vMD_NPM0Q8o3RE0a1OLUhxACnQ_-X0fYTWTCpdN3Shu2RBdVc1EspmgNymPNvSqnSXH8mB4K2WgraLMj9XcLew-jDqkqYNzFkrMZYgQ2AqfLDMIU4PmKym-dqyq9YyfNha7PfYpVs7-PgoerKsekPpvyF7Du7zvh1d4_Ir4ufD-dX9c3t5fX5j5saJGVj3bQN753WzDXcSkQlYSmgRXDOcQQuFetBKeiQ6SVXVDrOita1nROOKyuOyNnsu5m6AXvAMCa7NpvkyyPPJlpv_leCfzSruDVK6qaRuhgc7wxSfJowj2bwGXC9tgHjlA1nWpbgthUFrWcUUsw5oXuLYdS8zmDmGcw8Q-G_v__tjf7XewGWM4Cloa3HZDJ4LJX3PiGMpo_-A-sXa6mdlA</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Trebeschi, S.</creator><creator>Drago, S.G.</creator><creator>Birkbak, N.J.</creator><creator>Kurilova, I.</creator><creator>Cǎlin, A.M.</creator><creator>Delli Pizzi, A.</creator><creator>Lalezari, F.</creator><creator>Lambregts, D.M.J.</creator><creator>Rohaan, M.W.</creator><creator>Parmar, C.</creator><creator>Rozeman, E.A.</creator><creator>Hartemink, K.J.</creator><creator>Swanton, C.</creator><creator>Haanen, J B A G</creator><creator>Blank, C.U.</creator><creator>Smit, E.F.</creator><creator>Beets-Tan, R.G.H.</creator><creator>Aerts, H.J.W.L</creator><general>Elsevier Ltd</general><general>Oxford University Press</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20190601</creationdate><title>Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers</title><author>Trebeschi, S. ; Drago, S.G. ; Birkbak, N.J. ; Kurilova, I. ; Cǎlin, A.M. ; Delli Pizzi, A. ; Lalezari, F. ; Lambregts, D.M.J. ; Rohaan, M.W. ; Parmar, C. ; Rozeman, E.A. ; Hartemink, K.J. ; Swanton, C. ; Haanen, J B A G ; Blank, C.U. ; Smit, E.F. ; Beets-Tan, R.G.H. ; Aerts, H.J.W.L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c501t-4842df991f42a5ee65c73c8ecfff2ec2561dc66cbe1972605f21cffb8bf3f26a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Antineoplastic Combined Chemotherapy Protocols - therapeutic use</topic><topic>Artificial Intelligence</topic><topic>Carcinoma, Non-Small-Cell Lung - diagnostic imaging</topic><topic>Carcinoma, Non-Small-Cell Lung - drug therapy</topic><topic>Carcinoma, Non-Small-Cell Lung - immunology</topic><topic>Carcinoma, Non-Small-Cell Lung - pathology</topic><topic>Editor's Choice</topic><topic>Follow-Up Studies</topic><topic>Humans</topic><topic>immunotherapy</topic><topic>Immunotherapy - methods</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>Lung Neoplasms - drug therapy</topic><topic>Lung Neoplasms - immunology</topic><topic>Lung Neoplasms - pathology</topic><topic>Machine Learning</topic><topic>medical imaging</topic><topic>Melanoma - diagnostic imaging</topic><topic>Melanoma - drug therapy</topic><topic>Melanoma - immunology</topic><topic>Melanoma - pathology</topic><topic>Original articles</topic><topic>Predictive Value of Tests</topic><topic>Prognosis</topic><topic>Programmed Cell Death 1 Receptor - antagonists & inhibitors</topic><topic>Programmed Cell Death 1 Receptor - immunology</topic><topic>radiomics</topic><topic>response prediction</topic><topic>Survival Rate</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Trebeschi, S.</creatorcontrib><creatorcontrib>Drago, S.G.</creatorcontrib><creatorcontrib>Birkbak, N.J.</creatorcontrib><creatorcontrib>Kurilova, I.</creatorcontrib><creatorcontrib>Cǎlin, A.M.</creatorcontrib><creatorcontrib>Delli Pizzi, A.</creatorcontrib><creatorcontrib>Lalezari, F.</creatorcontrib><creatorcontrib>Lambregts, D.M.J.</creatorcontrib><creatorcontrib>Rohaan, M.W.</creatorcontrib><creatorcontrib>Parmar, C.</creatorcontrib><creatorcontrib>Rozeman, E.A.</creatorcontrib><creatorcontrib>Hartemink, K.J.</creatorcontrib><creatorcontrib>Swanton, C.</creatorcontrib><creatorcontrib>Haanen, J B A G</creatorcontrib><creatorcontrib>Blank, C.U.</creatorcontrib><creatorcontrib>Smit, E.F.</creatorcontrib><creatorcontrib>Beets-Tan, R.G.H.</creatorcontrib><creatorcontrib>Aerts, H.J.W.L</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Annals of oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Trebeschi, S.</au><au>Drago, S.G.</au><au>Birkbak, N.J.</au><au>Kurilova, I.</au><au>Cǎlin, A.M.</au><au>Delli Pizzi, A.</au><au>Lalezari, F.</au><au>Lambregts, D.M.J.</au><au>Rohaan, M.W.</au><au>Parmar, C.</au><au>Rozeman, E.A.</au><au>Hartemink, K.J.</au><au>Swanton, C.</au><au>Haanen, J B A G</au><au>Blank, C.U.</au><au>Smit, E.F.</au><au>Beets-Tan, R.G.H.</au><au>Aerts, H.J.W.L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers</atitle><jtitle>Annals of oncology</jtitle><addtitle>Ann Oncol</addtitle><date>2019-06-01</date><risdate>2019</risdate><volume>30</volume><issue>6</issue><spage>998</spage><epage>1004</epage><pages>998-1004</pages><issn>0923-7534</issn><eissn>1569-8041</eissn><abstract>Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds—urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.
In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.
The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P<0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P=0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P<0.001), resulting in a 1-year survival difference of 24% (P=0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.
These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>30895304</pmid><doi>10.1093/annonc/mdz108</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Antineoplastic Combined Chemotherapy Protocols - therapeutic use Artificial Intelligence Carcinoma, Non-Small-Cell Lung - diagnostic imaging Carcinoma, Non-Small-Cell Lung - drug therapy Carcinoma, Non-Small-Cell Lung - immunology Carcinoma, Non-Small-Cell Lung - pathology Editor's Choice Follow-Up Studies Humans immunotherapy Immunotherapy - methods Lung Neoplasms - diagnostic imaging Lung Neoplasms - drug therapy Lung Neoplasms - immunology Lung Neoplasms - pathology Machine Learning medical imaging Melanoma - diagnostic imaging Melanoma - drug therapy Melanoma - immunology Melanoma - pathology Original articles Predictive Value of Tests Prognosis Programmed Cell Death 1 Receptor - antagonists & inhibitors Programmed Cell Death 1 Receptor - immunology radiomics response prediction Survival Rate Tomography, X-Ray Computed - methods |
| title | Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers |
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