Ayşegül Aksu , Zeynep Gülsüm Güç , Kadir Alper Küçüker , Ahmet Alacacıoğlu , Bülent Turgut
{"title":"通过瘤内和瘤周 PET 放射组学分析预测接受新辅助化疗的乳腺癌患者的病理反应。","authors":"Ayşegül Aksu , Zeynep Gülsüm Güç , Kadir Alper Küçüker , Ahmet Alacacıoğlu , Bülent Turgut","doi":"10.1016/j.remnie.2024.500002","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC).</p></div><div><h3>Methods</h3><p>Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-<sub>T</sub>) was manually segmented, then a voxel-thick VOI was added around VOI-<sub>T</sub> to define the peritumoral area (VOI-<sub>PT</sub>). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A “radiomic model” was created with only radiomic features, and a “patho-radiomic model” was created using radiomic features and immunohistochemical data.</p></div><div><h3>Results</h3><p>Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-<sub>T</sub> (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-<sub>PT</sub>; no correlation was found between Morphological_Compacity-<sub>PT</sub> and NGTDM_contrast-<sub>PT</sub>. The obtained radiomic model’s sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903).</p></div><div><h3>Conclusions</h3><p>Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.</p></div>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy\",\"authors\":\"Ayşegül Aksu , Zeynep Gülsüm Güç , Kadir Alper Küçüker , Ahmet Alacacıoğlu , Bülent Turgut\",\"doi\":\"10.1016/j.remnie.2024.500002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC).</p></div><div><h3>Methods</h3><p>Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-<sub>T</sub>) was manually segmented, then a voxel-thick VOI was added around VOI-<sub>T</sub> to define the peritumoral area (VOI-<sub>PT</sub>). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A “radiomic model” was created with only radiomic features, and a “patho-radiomic model” was created using radiomic features and immunohistochemical data.</p></div><div><h3>Results</h3><p>Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-<sub>T</sub> (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-<sub>PT</sub>; no correlation was found between Morphological_Compacity-<sub>PT</sub> and NGTDM_contrast-<sub>PT</sub>. The obtained radiomic model’s sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903).</p></div><div><h3>Conclusions</h3><p>Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.</p></div>\",\"PeriodicalId\":94197,\"journal\":{\"name\":\"Revista espanola de medicina nuclear e imagen molecular\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revista espanola de medicina nuclear e imagen molecular\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2253808924000156\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista espanola de medicina nuclear e imagen molecular","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2253808924000156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy
Objective
The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC).
Methods
Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A “radiomic model” was created with only radiomic features, and a “patho-radiomic model” was created using radiomic features and immunohistochemical data.
Results
Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model’s sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903).
Conclusions
Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.