PMID- 35385985 OWN - NLM STAT- MEDLINE DCOM- 20220818 LR - 20231213 IS - 1432-1084 (Electronic) IS - 0938-7994 (Print) IS - 0938-7994 (Linking) VI - 32 IP - 9 DP - 2022 Sep TI - FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study. PG - 5921-5929 LID - 10.1007/s00330-022-08725-3 [doi] AB - OBJECTIVES: To evaluate if radiomics with machine learning can differentiate between F-18-fluorodeoxyglucose (FDG)-avid breast cancer metastatic lymphadenopathy and FDG-avid COVID-19 mRNA vaccine-related axillary lymphadenopathy. MATERIALS AND METHODS: We retrospectively analyzed FDG-positive, pathology-proven, metastatic axillary lymph nodes in 53 breast cancer patients who had PET/CT for follow-up or staging, and FDG-positive axillary lymph nodes in 46 patients who were vaccinated with the COVID-19 mRNA vaccine. Radiomics features (110 features classified into 7 groups) were extracted from all segmented lymph nodes. Analysis was performed on PET, CT, and combined PET/CT inputs. Lymph nodes were randomly assigned to a training (n = 132) and validation cohort (n = 33) by 5-fold cross-validation. K-nearest neighbors (KNN) and random forest (RF) machine learning models were used. Performance was evaluated using an area under the receiver-operator characteristic curve (AUC-ROC) score. RESULTS: Axillary lymph nodes from breast cancer patients (n = 85) and COVID-19-vaccinated individuals (n = 80) were analyzed. Analysis of first-order features showed statistically significant differences (p < 0.05) in all combined PET/CT features, most PET features, and half of the CT features. The KNN model showed the best performance score for combined PET/CT and PET input with 0.98 (+/- 0.03) and 0.88 (+/- 0.07) validation AUC, and 96% (+/- 4%) and 85% (+/- 9%) validation accuracy, respectively. The RF model showed the best result for CT input with 0.96 (+/- 0.04) validation AUC and 90% (+/- 6%) validation accuracy. CONCLUSION: Radiomics features can differentiate between FDG-avid breast cancer metastatic and FDG-avid COVID-19 vaccine-related axillary lymphadenopathy. Such a model may have a role in differentiating benign nodes from malignant ones. KEY POINTS: * Patients who were vaccinated with the COVID-19 mRNA vaccine have shown FDG-avid reactive axillary lymph nodes in PET-CT scans. * We evaluated if radiomics and machine learning can distinguish between FDG-avid metastatic axillary lymphadenopathy in breast cancer patients and FDG-avid reactive axillary lymph nodes. * Combined PET and CT radiomics data showed good test AUC (0.98) for distinguishing between metastatic axillary lymphadenopathy and post-COVID-19 vaccine-associated axillary lymphadenopathy. Therefore, the use of radiomics may have a role in differentiating between benign from malignant FDG-avid nodes. CI - (c) 2022. The Author(s), under exclusive licence to European Society of Radiology. FAU - Eifer, Michal AU - Eifer M AUID- ORCID: 0000-0002-1111-7875 AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. michaleifer@gmail.com. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. michaleifer@gmail.com. FAU - Pinian, Hodaya AU - Pinian H AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. FAU - Klang, Eyal AU - Klang E AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. AD - ARC Center for Digital Innovation, Chaim Sheba Medical Center, Ramat Gan, Israel. FAU - Alhoubani, Yousef AU - Alhoubani Y AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. FAU - Kanana, Nayroz AU - Kanana N AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. FAU - Tau, Noam AU - Tau N AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. FAU - Davidson, Tima AU - Davidson T AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. FAU - Konen, Eli AU - Konen E AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. FAU - Catalano, Onofrio A AU - Catalano OA AD - Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. FAU - Eshet, Yael AU - Eshet Y AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. FAU - Domachevsky, Liran AU - Domachevsky L AD - Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel. AD - Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. LA - eng PT - Journal Article DEP - 20220406 PL - Germany TA - Eur Radiol JT - European radiology JID - 9114774 RN - 0 (COVID-19 Vaccines) RN - 0 (Vaccines, Synthetic) RN - 0 (mRNA Vaccines) RN - 0Z5B2CJX4D (Fluorodeoxyglucose F18) SB - IM MH - *Breast Neoplasms/pathology MH - *COVID-19 MH - COVID-19 Vaccines/adverse effects MH - Female MH - Fluorodeoxyglucose F18 MH - Humans MH - Lymph Nodes/diagnostic imaging/pathology MH - *Lymphadenopathy/diagnostic imaging/etiology/pathology MH - Lymphatic Metastasis/pathology MH - Pilot Projects MH - Positron Emission Tomography Computed Tomography MH - Retrospective Studies MH - Vaccination MH - Vaccines, Synthetic MH - mRNA Vaccines PMC - PMC8985565 OTO - NOTNLM OT - Breast cancer OT - COVID-19 vaccine OT - Lymphadenopathy OT - Machine learning OT - PET-CT COIS- The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. EDAT- 2022/04/08 06:00 MHDA- 2022/08/19 06:00 PMCR- 2022/04/06 CRDT- 2022/04/07 05:03 PHST- 2022/01/30 00:00 [received] PHST- 2022/03/09 00:00 [accepted] PHST- 2022/03/06 00:00 [revised] PHST- 2022/04/08 06:00 [pubmed] PHST- 2022/08/19 06:00 [medline] PHST- 2022/04/07 05:03 [entrez] PHST- 2022/04/06 00:00 [pmc-release] AID - 10.1007/s00330-022-08725-3 [pii] AID - 8725 [pii] AID - 10.1007/s00330-022-08725-3 [doi] PST - ppublish SO - Eur Radiol. 2022 Sep;32(9):5921-5929. doi: 10.1007/s00330-022-08725-3. Epub 2022 Apr 6.