Anita Florit,Wyanne A Noortman,Nicolò Bizzarri,Tina Pasciuto,Vanessa Feudo,Salvatore Annunziata,Lioe-Fee de Geus-Oei,Elisabeth Pfaehler,Ronald Boellaard,Maria Antonietta Gambacorta,Gian Franco Zannoni,Gabriella Ferrandina,Evis Sala,Giovanni Scambia,Vittoria Rufini,Floris H P van Velden,Angela Collarino
{"title":"局部晚期宫颈癌早期和三角洲PET放射学特征预测预后的探索性评估。","authors":"Anita Florit,Wyanne A Noortman,Nicolò Bizzarri,Tina Pasciuto,Vanessa Feudo,Salvatore Annunziata,Lioe-Fee de Geus-Oei,Elisabeth Pfaehler,Ronald Boellaard,Maria Antonietta Gambacorta,Gian Franco Zannoni,Gabriella Ferrandina,Evis Sala,Giovanni Scambia,Vittoria Rufini,Floris H P van Velden,Angela Collarino","doi":"10.1007/s00259-025-07405-w","DOIUrl":null,"url":null,"abstract":"PURPOSE\r\nThis study investigated whether radiomic features extracted from [18F]FDG-PET scans acquired before and two weeks after neoadjuvant treatment, and their variation, provided prognostic parameters in locally advanced cervical cancer (LACC) patients treated with neoadjuvant chemo-radiotherapy (CRT) followed by radical surgery.\r\n\r\nMETHODS\r\nWe retrospectively included LACC patients referred to our Institution from 2010 to 2016. [18F]FDG-PET/CT was performed before neoadjuvant CRT (baseline) and two weeks after the start of treatment (early). Radiomic features were extracted after semi-automatic delineation of the primary tumour, on baseline and early PET images. Delta radiomics were calculated as the relative differences between baseline and early features. We performed 5-fold cross-validation stratified for recurrence and cancer-specific death, integrating dimensionality reduction of the radiomic features and variable hunting with importance within the folds. After supervised feature selection, radiomic models with the best-performing features for each timepoint, as well as clinical models and combined clinico-radiomic models, were built. Model performances are presented as C-indices, for prediction of recurrence/progression (disease-free survival, DFS) and cancer-specific death (overall survival, OS).\r\n\r\nRESULTS\r\n95 patients were included. With a median follow-up of 76.0 months (95% CI: 59.5-82.1), 31.6% of patients had recurrence/progression and 20.0% died of disease. None of the models could predict DFS (C-indices ≤ 0.72). Model performances for OS yielded slightly better results, with mean C-indices of 0.75 for both the radiomic and combined model based on early features, 0.79 and 0.78 for the radiomic and combined model derived from delta features, and 0.76 for the clinical models.\r\n\r\nCONCLUSION\r\n[18F]FDG-PET early and delta radiomic features could not predict DFS in patients with LACC treated with neoadjuvant CRT followed by radical surgery. Although slightly improved performances for the radiomic and combined models were observed in the prediction of OS compared to the clinical model, the added value of these parameters and their inclusion in the clinical practice seems to be limited.","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"40 1","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.\",\"authors\":\"Anita Florit,Wyanne A Noortman,Nicolò Bizzarri,Tina Pasciuto,Vanessa Feudo,Salvatore Annunziata,Lioe-Fee de Geus-Oei,Elisabeth Pfaehler,Ronald Boellaard,Maria Antonietta Gambacorta,Gian Franco Zannoni,Gabriella Ferrandina,Evis Sala,Giovanni Scambia,Vittoria Rufini,Floris H P van Velden,Angela Collarino\",\"doi\":\"10.1007/s00259-025-07405-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PURPOSE\\r\\nThis study investigated whether radiomic features extracted from [18F]FDG-PET scans acquired before and two weeks after neoadjuvant treatment, and their variation, provided prognostic parameters in locally advanced cervical cancer (LACC) patients treated with neoadjuvant chemo-radiotherapy (CRT) followed by radical surgery.\\r\\n\\r\\nMETHODS\\r\\nWe retrospectively included LACC patients referred to our Institution from 2010 to 2016. [18F]FDG-PET/CT was performed before neoadjuvant CRT (baseline) and two weeks after the start of treatment (early). Radiomic features were extracted after semi-automatic delineation of the primary tumour, on baseline and early PET images. Delta radiomics were calculated as the relative differences between baseline and early features. We performed 5-fold cross-validation stratified for recurrence and cancer-specific death, integrating dimensionality reduction of the radiomic features and variable hunting with importance within the folds. After supervised feature selection, radiomic models with the best-performing features for each timepoint, as well as clinical models and combined clinico-radiomic models, were built. Model performances are presented as C-indices, for prediction of recurrence/progression (disease-free survival, DFS) and cancer-specific death (overall survival, OS).\\r\\n\\r\\nRESULTS\\r\\n95 patients were included. With a median follow-up of 76.0 months (95% CI: 59.5-82.1), 31.6% of patients had recurrence/progression and 20.0% died of disease. None of the models could predict DFS (C-indices ≤ 0.72). Model performances for OS yielded slightly better results, with mean C-indices of 0.75 for both the radiomic and combined model based on early features, 0.79 and 0.78 for the radiomic and combined model derived from delta features, and 0.76 for the clinical models.\\r\\n\\r\\nCONCLUSION\\r\\n[18F]FDG-PET early and delta radiomic features could not predict DFS in patients with LACC treated with neoadjuvant CRT followed by radical surgery. Although slightly improved performances for the radiomic and combined models were observed in the prediction of OS compared to the clinical model, the added value of these parameters and their inclusion in the clinical practice seems to be limited.\",\"PeriodicalId\":11909,\"journal\":{\"name\":\"European Journal of Nuclear Medicine and Molecular Imaging\",\"volume\":\"40 1\",\"pages\":\"\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2025-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Nuclear Medicine and Molecular Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00259-025-07405-w\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Nuclear Medicine and Molecular Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00259-025-07405-w","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.
PURPOSE
This study investigated whether radiomic features extracted from [18F]FDG-PET scans acquired before and two weeks after neoadjuvant treatment, and their variation, provided prognostic parameters in locally advanced cervical cancer (LACC) patients treated with neoadjuvant chemo-radiotherapy (CRT) followed by radical surgery.
METHODS
We retrospectively included LACC patients referred to our Institution from 2010 to 2016. [18F]FDG-PET/CT was performed before neoadjuvant CRT (baseline) and two weeks after the start of treatment (early). Radiomic features were extracted after semi-automatic delineation of the primary tumour, on baseline and early PET images. Delta radiomics were calculated as the relative differences between baseline and early features. We performed 5-fold cross-validation stratified for recurrence and cancer-specific death, integrating dimensionality reduction of the radiomic features and variable hunting with importance within the folds. After supervised feature selection, radiomic models with the best-performing features for each timepoint, as well as clinical models and combined clinico-radiomic models, were built. Model performances are presented as C-indices, for prediction of recurrence/progression (disease-free survival, DFS) and cancer-specific death (overall survival, OS).
RESULTS
95 patients were included. With a median follow-up of 76.0 months (95% CI: 59.5-82.1), 31.6% of patients had recurrence/progression and 20.0% died of disease. None of the models could predict DFS (C-indices ≤ 0.72). Model performances for OS yielded slightly better results, with mean C-indices of 0.75 for both the radiomic and combined model based on early features, 0.79 and 0.78 for the radiomic and combined model derived from delta features, and 0.76 for the clinical models.
CONCLUSION
[18F]FDG-PET early and delta radiomic features could not predict DFS in patients with LACC treated with neoadjuvant CRT followed by radical surgery. Although slightly improved performances for the radiomic and combined models were observed in the prediction of OS compared to the clinical model, the added value of these parameters and their inclusion in the clinical practice seems to be limited.
期刊介绍:
The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.