[<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept
Despite impressive results, almost 30% of NET do not respond to PRRT and no well-established criteria are suitable to predict response. Therefore, we assessed the predictive value of radiomics [<sup>68</sup>Ga]DOTATOC PET/CT images pre-PRRT in metastatic GEP NET. We retrospectively analy...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/4/984 |
| _version_ | 1797482093726400512 |
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| author | Riccardo Laudicella Albert Comelli Virginia Liberini Antonio Vento Alessandro Stefano Alessandro Spataro Ludovica Crocè Sara Baldari Michelangelo Bambaci Desiree Deandreis Demetrio Arico’ Massimo Ippolito Michele Gaeta Pierpaolo Alongi Fabio Minutoli Irene A. Burger Sergio Baldari |
| author_facet | Riccardo Laudicella Albert Comelli Virginia Liberini Antonio Vento Alessandro Stefano Alessandro Spataro Ludovica Crocè Sara Baldari Michelangelo Bambaci Desiree Deandreis Demetrio Arico’ Massimo Ippolito Michele Gaeta Pierpaolo Alongi Fabio Minutoli Irene A. Burger Sergio Baldari |
| author_sort | Riccardo Laudicella |
| collection | DOAJ |
| description | Despite impressive results, almost 30% of NET do not respond to PRRT and no well-established criteria are suitable to predict response. Therefore, we assessed the predictive value of radiomics [<sup>68</sup>Ga]DOTATOC PET/CT images pre-PRRT in metastatic GEP NET. We retrospectively analyzed the predictive value of radiomics in 324 SSTR-2-positive lesions from 38 metastatic GEP-NET patients (nine G1, 27 G2, and two G3) who underwent restaging [<sup>68</sup>Ga]DOTATOC PET/CT before complete PRRT with [<sup>177</sup>Lu]DOTATOC. Clinical, laboratory, and radiological follow-up data were collected for at least six months after the last cycle. Through LifeX, we extracted 65 PET features for each lesion. Grading, PRRT number of cycles, and cumulative activity, pre- and post-PRRT CgA values were also considered as additional clinical features. [<sup>68</sup>Ga]DOTATOC PET/CT follow-up with the same scanner for each patient determined the disease status (progression vs. response in terms of stability/reduction/disappearance) for each lesion. All features (PET and clinical) were also correlated with follow-up data in a per-site analysis (liver, lymph nodes, and bone), and for features significantly associated with response, the Δradiomics for each lesion was assessed on follow-up [<sup>68</sup>Ga]DOTATOC PET/CT performed until nine months post-PRRT. A statistical system based on the point-biserial correlation and logistic regression analysis was used for the reduction and selection of the features. Discriminant analysis was used, instead, to obtain the predictive model using the k-fold strategy to split data into training and validation sets. From the reduction and selection process, HISTO_Skewness and HISTO_Kurtosis were able to predict response with an area under the receiver operating characteristics curve (AUC ROC), sensitivity, and specificity of 0.745, 80.6%, 67.2% and 0.722, 61.2%, 75.9%, respectively. Moreover, a combination of three features (HISTO_Skewness; HISTO_Kurtosis, and Grading) did not improve the AUC significantly with 0.744. SUV<sub>max</sub>, however, could not predict the response to PRRT (<i>p</i> = 0.49, AUC 0.523). The presented preliminary “theragnomics” model proved to be superior to conventional quantitative parameters to predict the response of GEP-NET lesions in patients treated with complete [<sup>177</sup>Lu]DOTATOC PRRT, regardless of the lesion site. |
| first_indexed | 2024-03-09T22:24:21Z |
| format | Article |
| id | doaj.art-ab3a23b5d57244deae6bb4e30af5cab0 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T22:24:21Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-ab3a23b5d57244deae6bb4e30af5cab02023-11-23T19:09:34ZengMDPI AGCancers2072-66942022-02-0114498410.3390/cancers14040984[<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” ConceptRiccardo Laudicella0Albert Comelli1Virginia Liberini2Antonio Vento3Alessandro Stefano4Alessandro Spataro5Ludovica Crocè6Sara Baldari7Michelangelo Bambaci8Desiree Deandreis9Demetrio Arico’10Massimo Ippolito11Michele Gaeta12Pierpaolo Alongi13Fabio Minutoli14Irene A. Burger15Sergio Baldari16Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, ItalyRi.MED Foundation, 90134 Palermo, ItalyNuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, ItalyNuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, ItalyInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, ItalyNuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, ItalyNuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, ItalyNuclear Medicine Department, Cannizzaro Hospital, 95126 Catania, ItalyDepartment of Nuclear Medicine, Humanitas Oncological Centre of Catania, 95125 Catania, ItalyNuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, ItalyDepartment of Nuclear Medicine, Humanitas Oncological Centre of Catania, 95125 Catania, ItalyNuclear Medicine Department, Cannizzaro Hospital, 95126 Catania, ItalySection of Radiological Sciences, Department of Biomedical Sciences and Morphological and Functional Imaging, University of Messina, 98125 Messina, ItalyNuclear Medicine Unit, Fondazione Istituto G.Giglio, 90015 Cefalù, ItalyNuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, ItalyDepartment of Nuclear Medicine, University Hospital Zürich, University of Zürich, 8091 Zürich, SwitzerlandNuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, ItalyDespite impressive results, almost 30% of NET do not respond to PRRT and no well-established criteria are suitable to predict response. Therefore, we assessed the predictive value of radiomics [<sup>68</sup>Ga]DOTATOC PET/CT images pre-PRRT in metastatic GEP NET. We retrospectively analyzed the predictive value of radiomics in 324 SSTR-2-positive lesions from 38 metastatic GEP-NET patients (nine G1, 27 G2, and two G3) who underwent restaging [<sup>68</sup>Ga]DOTATOC PET/CT before complete PRRT with [<sup>177</sup>Lu]DOTATOC. Clinical, laboratory, and radiological follow-up data were collected for at least six months after the last cycle. Through LifeX, we extracted 65 PET features for each lesion. Grading, PRRT number of cycles, and cumulative activity, pre- and post-PRRT CgA values were also considered as additional clinical features. [<sup>68</sup>Ga]DOTATOC PET/CT follow-up with the same scanner for each patient determined the disease status (progression vs. response in terms of stability/reduction/disappearance) for each lesion. All features (PET and clinical) were also correlated with follow-up data in a per-site analysis (liver, lymph nodes, and bone), and for features significantly associated with response, the Δradiomics for each lesion was assessed on follow-up [<sup>68</sup>Ga]DOTATOC PET/CT performed until nine months post-PRRT. A statistical system based on the point-biserial correlation and logistic regression analysis was used for the reduction and selection of the features. Discriminant analysis was used, instead, to obtain the predictive model using the k-fold strategy to split data into training and validation sets. From the reduction and selection process, HISTO_Skewness and HISTO_Kurtosis were able to predict response with an area under the receiver operating characteristics curve (AUC ROC), sensitivity, and specificity of 0.745, 80.6%, 67.2% and 0.722, 61.2%, 75.9%, respectively. Moreover, a combination of three features (HISTO_Skewness; HISTO_Kurtosis, and Grading) did not improve the AUC significantly with 0.744. SUV<sub>max</sub>, however, could not predict the response to PRRT (<i>p</i> = 0.49, AUC 0.523). The presented preliminary “theragnomics” model proved to be superior to conventional quantitative parameters to predict the response of GEP-NET lesions in patients treated with complete [<sup>177</sup>Lu]DOTATOC PRRT, regardless of the lesion site.https://www.mdpi.com/2072-6694/14/4/984<sup>177</sup>Luartificial intelligence[<sup>68</sup>Ga]DOTATOC PETGEP NETmachine-learningPRRT |
| spellingShingle | Riccardo Laudicella Albert Comelli Virginia Liberini Antonio Vento Alessandro Stefano Alessandro Spataro Ludovica Crocè Sara Baldari Michelangelo Bambaci Desiree Deandreis Demetrio Arico’ Massimo Ippolito Michele Gaeta Pierpaolo Alongi Fabio Minutoli Irene A. Burger Sergio Baldari [<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept Cancers <sup>177</sup>Lu artificial intelligence [<sup>68</sup>Ga]DOTATOC PET GEP NET machine-learning PRRT |
| title | [<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept |
| title_full | [<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept |
| title_fullStr | [<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept |
| title_full_unstemmed | [<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept |
| title_short | [<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept |
| title_sort | sup 68 sup ga dotatoc pet ct radiomics to predict the response in gep nets undergoing sup 177 sup lu dotatoc prrt the theragnomics concept |
| topic | <sup>177</sup>Lu artificial intelligence [<sup>68</sup>Ga]DOTATOC PET GEP NET machine-learning PRRT |
| url | https://www.mdpi.com/2072-6694/14/4/984 |
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