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000169963 1001_ $$00000-0003-2401-8240$$aCarles, Montserrat$$b0
000169963 245__ $$a18F-FMISO-PET Hypoxia Monitoring for Head-and-Neck Cancer Patients: Radiomics Analyses Predict the Outcome of Chemo-Radiotherapy.
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000169963 520__ $$aTumor hypoxia is associated with radiation resistance and can be longitudinally monitored by 18F-fluoromisonidazole (18F-FMISO)-PET/CT. Our study aimed at evaluating radiomics dynamics of 18F-FMISO-hypoxia imaging during chemo-radiotherapy (CRT) as predictors for treatment outcome in head-and-neck squamous cell carcinoma (HNSCC) patients. We prospectively recruited 35 HNSCC patients undergoing definitive CRT and longitudinal 18F-FMISO-PET/CT scans at weeks 0, 2 and 5 (W0/W2/W5). Patients were classified based on peritherapeutic variations of the hypoxic sub-volume (HSV) size (increasing/stable/decreasing) and location (geographically-static/geographically-dynamic) by a new objective classification parameter (CP) accounting for spatial overlap. Additionally, 130 radiomic features (RF) were extracted from HSV at W0, and their variations during CRT were quantified by relative deviations (∆RF). Prediction of treatment outcome was considered statistically relevant after being corrected for multiple testing and confirmed for the two 18F-FMISO-PET/CT time-points and for a validation cohort. HSV decreased in 64% of patients at W2 and in 80% at W5. CP distinguished earlier disease progression (geographically-dynamic) from later disease progression (geographically-static) in both time-points and cohorts. The texture feature low grey-level zone emphasis predicted local recurrence with AUCW2 = 0.82 and AUCW5 = 0.81 in initial cohort (N = 25) and AUCW2 = 0.79 and AUCW5 = 0.80 in validation cohort. Radiomics analysis of 18F-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT.
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000169963 650_7 $$2Other$$a18F-FMISO-PET
000169963 650_7 $$2Other$$ahead-and-neck squamous cell carcinoma and radiomics
000169963 650_7 $$2Other$$ahypoxia
000169963 650_7 $$2Other$$aradiotherapy response monitoring
000169963 7001_ $$00000-0001-6271-9385$$aFechter, Tobias$$b1
000169963 7001_ $$0P:(DE-HGF)0$$aGrosu, Anca L$$b2
000169963 7001_ $$0P:(DE-HGF)0$$aSörensen, Arnd$$b3
000169963 7001_ $$0P:(DE-HGF)0$$aThomann, Benedikt$$b4
000169963 7001_ $$0P:(DE-He78)75b4c256a6de824414938cf2aaeff88e$$aStoian, Raluca$$b5
000169963 7001_ $$0P:(DE-HGF)0$$aWiedenmann, Nicole$$b6
000169963 7001_ $$0P:(DE-HGF)0$$aRühle, Alexander$$b7
000169963 7001_ $$0P:(DE-HGF)0$$aZamboglou, Constantinos$$b8
000169963 7001_ $$0P:(DE-HGF)0$$aRuf, Juri$$b9
000169963 7001_ $$00000-0002-8234-010X$$aMartí-Bonmatí, Luis$$b10
000169963 7001_ $$00000-0003-4220-9083$$aBaltas, Dimos$$b11
000169963 7001_ $$00000-0002-9106-2519$$aMix, Michael$$b12
000169963 7001_ $$0P:(DE-HGF)0$$aNicolay, Nils H$$b13$$eLast author
000169963 773__ $$0PERI:(DE-600)2527080-1$$a10.3390/cancers13143449$$gVol. 13, no. 14, p. 3449 -$$n14$$p3449$$tCancers$$v13$$x2072-6694$$y2021
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