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'pictures,': [987], 'data.': [990], 'Radiology': [991, 1106, 1135], '2016;278(2):563–577.': [992], 'Link,': [993, 1108, 1137], 'Google': [994, 1020, 1046, 1082, 1109, 1138], 'Scholar2.': [995], 'Aerts': [996], 'HJ,': [997], 'Velazquez': [998], 'ER,': [999], 'Leijenaar': [1000, 1024], 'RT,': [1001], 'al.': [1003, 1029, 1055, 1091, 1118], 'Decoding': [1004], 'tumour': [1005], 'phenotype': [1006], 'approach.': [1014], 'Nat': [1015, 1039], 'Commun': [1016], '2014;5(1):4006.': [1017], 'Crossref,': [1018, 1044, 1080], 'Medline,': [1019, 1045, 1081], 'Scholar3.': [1021], 'Lambin': [1022], 'P,': [1023], 'RTH,': [1025], 'Deist': [1026], 'TM,': [1027], 'bridge': [1032], 'personalized': [1037], 'medicine.': [1038], 'Rev': [1040], 'Clin': [1041], 'Oncol': [1042], '2017;14(12):749–762.': [1043], 'Scholar4.': [1047], 'Kim': [1048], 'H,': [1049], 'CM,': [1051], 'Lee': [1052, 1113], 'M,': [1053], 'Impact': [1056, 1685], 'tumors:': [1066], 'intra-': [1069], 'inter-reader': [1071], 'inter-reconstruction': [1074], 'variability.': [1076], 'PLoS': [1077], 'One': [1078], '2016;11(10):e0164924.': [1079], 'Scholar5.': [1083], 'Berenguer': [1084], 'R,': [1085], 'Pastor-Juan': [1086], 'MDR,': [1087], 'Canales-Vázquez': [1088], 'J,': [1089, 1112], 'nonreproducible': [1098], 'redundant:': [1100], '2018;288(2):407–415.': [1107], 'Scholar6.': [1110], 'SM,': [1114], 'Do': [1115], 'KH,': [1116], 'learning-based': [1120], 'improves': [1127], 'masses.': [1134], '2019;292;365–373.': [1136], 'ScholarArticle': [1139], 'HistoryReceived:': [1140], 'May': [1141, 1145, 1149, 1152, 1296], '21': [1142], '2019Revision': [1143, 1147], 'requested:': [1144], '28': [1146, 1150], 'received:': [1148], '2019Accepted:': [1151], '29': [1153], '2019Published': [1154, 1158], 'online:': [1155], 'June': [1156], 'print:': [1160], 'Aug': [1161], '2019': [1162], 'FiguresReferencesRelatedDetailsCited': [1163], 'ByGenerative': [1164], 'adversarial': [1165], 'computed': [1173], 'tomography': [1174], 'denoisingJinaLee,': [1175], 'JaeikJeon,': [1176], 'YoungtaekHong,': [1177, 1439], 'DawunJeong,': [1178], 'YeonggulJang,': [1179], 'ByunghwanJeon,': [1180], 'Hye': [1181, 1292], 'JinBaek,': [1182], 'EunCho,': [1183], 'HackjoonShim,': [1184], 'Hyuk-JaeChang2023': [1185], '|': [1186, 1226, 1258, 1298, 1322, 1359, 1393, 1420, 1445, 1479, 1500, 1540, 1580, 1603, 1627], 'Computers': [1187], 'Biology': [1189], 'Vol.': [1192, 1228, 1262, 1302, 1326, 1363, 1397, 1422, 1487, 1505, 1542, 1584, 1606, 1633, 1788], '159Annotation-Efficient': [1193], 'Learning': [1195, 1400, 1688, 1698, 1736, 1761], 'Model': [1196], 'Pancreatic': [1198], 'Cancer': [1199, 1408, 1482], 'Diagnosis': [1200, 1268], 'Classification': [1202], 'Using': [1203, 1271, 1409, 1732], 'Images:': [1205], 'Retrospective': [1207], 'Diagnostic': [1208, 1749], 'StudyThanapornViriyasaranon,': [1209], 'Jung': [1210], 'WonChun,': [1211], 'Young': [1212], 'HwanKoh,': [1213], 'Jae': [1214], 'HeeCho,': [1215], 'KyuJung,': [1217], 'Seong-HunKim,': [1218], 'Hyo': [1219], 'JungKim,': [1220], 'Woo': [1221], 'JinLee,': [1222], 'Jang-HwanChoi,': [1223], 'Sang': [1224], 'MyungWoo2023': [1225], 'Cancers,': [1227, 1421], '15,': [1229], '13Assessing': [1231], 'predictability': [1233], 'H3K27M': [1236], 'status': [1237], 'diffuse': [1239], 'glioma': [1240], 'frequency': [1243], 'importance': [1244], 'chemical': [1247], 'exchange': [1248], 'saturation': [1249], 'transfer': [1250], 'MRIYibingChen,': [1251], 'BenqiZhao,': [1252], 'ChanghaoZhu,': [1253], 'ChongxueBie,': [1254], 'XiaoweiHe,': [1255], 'ZhuozhaoZheng,': [1256], 'XiaoleiSong2023': [1257], 'Magnetic': [1259], 'Resonance': [1260], 'Imaging,': [1261, 1632], '103Deep': [1263], 'Radiomics–based': [1264], 'Approach': [1265], 'Osteoporosis': [1270], 'Hip': [1272], 'RadiographsSangwook': [1273], 'Kim,': [1274, 1277, 1286], 'Bo': [1275], 'Ram': [1276], 'Hee-Dong': [1278], 'Chae,': [1279], 'Jimin': [1280], 'Lee,': [1281], 'Sung-Joon': [1282], 'Ye,': [1283], 'Dong': [1284], 'Hyun': [1285], 'Sung': [1287], 'Hwan': [1288], 'Hong,': [1289], 'Ja-Young': [1290], 'Choi,': [1291], 'Jin': [1293], 'Yoo,': [1294], '25': [1295], '2022': [1297], 'Radiology:': [1299, 1581], 'Intelligence,': [1301], '4,': [1303], '4A': [1305], 'meta-analysis': [1306], 'diagnostic': [1309], 'test': [1310], 'accuracy': [1311], 'CT-based': [1313], 'prediction': [1317], 'COVID-19': [1319], 'severityYung-ShuoKao,': [1320], 'Kun-TeLin2022': [1321], 'La': [1323], 'radiologia': [1324], 'medica,': [1325], '127,': [1327], '718F-FDG': [1329], 'PET': [1330], 'Predictor': [1333], 'Treatment': [1335], 'Response': [1336, 1367], 'Oesophageal': [1338], 'Cancer:': [1339], 'Systematic': [1341, 1375], 'Review': [1342, 1376], 'Meta-AnalysisLetiziaDeantonio,': [1344], 'Maria': [1345, 1348, 1354], 'LuisaGaro,': [1346], 'GaetanoPaone,': [1347], 'CarlaValli,': [1349], 'StefanoCappio,': [1350], 'DavideLa': [1351], 'Regina,': [1352], 'MarcoCefali,': [1353], 'CelestePalmarocchi,': [1355], 'AlbertoVannelli,': [1356], 'SaraDe': [1357], 'Dosso2022': [1358], 'Frontiers': [1360, 1394], 'Oncology,': [1362, 1396, 1486], '12Radiomics': [1364], 'Predicting': [1366], 'Neoadjuvant': [1369], 'Chemotherapy': [1370], 'Nasopharyngeal': [1372], 'Carcinoma:': [1373], 'Meta-AnalysisChaoYang,': [1378], 'ZekunJiang,': [1379], 'TingtingCheng,': [1380], 'RongrongZhou,': [1381], 'GuangcanWang,': [1382], 'DiJing,': [1383], 'LinlinBo,': [1384], 'PuHuang,': [1385], 'JianboWang,': [1386], 'DaizhouZhang,': [1387], 'JianweiJiang,': [1388], 'XingWang,': [1389], 'HuaLu,': [1390], 'ZijianZhang,': [1391], 'DengwangLi2022': [1392], '12Radiomics-Based': [1398], 'Prediction': [1401], 'Overall': [1403], 'Survival': [1404], 'Non-Small-Cell': [1406], 'Lung': [1407, 1513], 'Contrast-Enhanced': [1410], 'Computed': [1411, 1434], 'TomographyKuei-YuanHou,': [1412], 'Jyun-RuChen,': [1413], 'Yung-ChenWang,': [1414], 'Ming-HuangChiu,': [1415], 'Sen-PingLin,': [1416], 'Yuan-HengMo,': [1417], 'Shih-ChiehPeng,': [1418], 'Chia-FengLu2022': 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