2024
DOI: 10.3390/jpm14010071
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Prognostic Value of Radiomic Analysis Using Pre- and Post-Treatment 18F-FDG-PET/CT in Patients with Laryngeal Cancer and Hypopharyngeal Cancer

Joon Ho Choi,
Joon Young Choi,
Sang-Keun Woo
et al.

Abstract: Background: The prognostic value of conducting 18F-FDG PET/CT imaging has yielded different results in patients with laryngeal cancer and hypopharyngeal cancer, but these results are controversial, and there is a lack of dedicated studies on each type of cancer. This study aimed to evaluate whether combining radiomic analysis of pre- and post-treatment 18F-FDG PET/CT imaging features and clinical parameters has additional prognostic value in patients with laryngeal cancer and hypopharyngeal cancer. Methods: Fr… Show more

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“…Recent studies and meta-analyses have demonstrated a reasonable performance for outcome models obtained from radiomics analysis in HNC [19], confirming that more homogeneous tumors have a better prognosis [188][189][190][191] and that the combination of radiomics and clinical information excellently predicts PFS and OS [192]. Moreover, in patients with ongoing radiotherapy, baseline to post-treatment differences in PET/CT radiomics may predict PFS and OS, irrespective of clinical parameters and T and N stage [193]. Radiomics analysis also performs better than clinicopathological factors in predicting cervical lymph node metastases [194].…”
Section: Application Of Radiomics and Machine Learningmentioning
confidence: 81%
“…Recent studies and meta-analyses have demonstrated a reasonable performance for outcome models obtained from radiomics analysis in HNC [19], confirming that more homogeneous tumors have a better prognosis [188][189][190][191] and that the combination of radiomics and clinical information excellently predicts PFS and OS [192]. Moreover, in patients with ongoing radiotherapy, baseline to post-treatment differences in PET/CT radiomics may predict PFS and OS, irrespective of clinical parameters and T and N stage [193]. Radiomics analysis also performs better than clinicopathological factors in predicting cervical lymph node metastases [194].…”
Section: Application Of Radiomics and Machine Learningmentioning
confidence: 81%