Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer

Lv, Wenbing; Xu, Hui; Xu, Han; Zhang, Hao; Ma, Jianhua; Rahmim, Arman; Lu, Lijun

doi:10.3390/cancers14071674

Cited by 12 publications

(10 citation statements)

References 46 publications

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“…This suggests that we may need to establish fusion model and validate the model on multi-center external data as in Ref. 62 Based on the findings of our study, fusion dosiomics method is helpful in predicting the prognosis of NPC. The features from primary tumor and the involved lymph node have different effects on prognosis and should be considered separately.…”

Section: F I G U R Ementioning

confidence: 80%

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Prognostic generalization of multi‐level CT‐dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma

Cai

Chen

et al. 2022

Medical Physics

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To investigate the prognostic performance of multi-level computed tomography (CT)-dose fusion dosiomics at the image-, matrix-, and featurelevels from the gross tumor volume (GTV) at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients. Methods: Two hundred and nineteen NPC patients (175 vs. 44 for training vs. internal validation) were used to train prediction model, and 32 NPC patients were used for external validation. We first extracted CT and dose information from intratumoral nasopharynx (GTV_nx) and lymph node (GTV_nd) regions. Then, the corresponding peritumoral regions (RING_3 mm and RING_5 mm) were also considered. Thus, the individual and combination of intratumoral and peritumoral regions were as follows: GTV_nx, GTV_nd, RING_3 mm_nx, RING_3 mm_nd, RING_5 mm_nx, RING_5 mm_nd, GTV_nxnd, RING_3 mm_nxnd, RING_5 mm_nxnd, GTV + RING_3 mm_nxnd, and GTV + RING_5 mm_nxnd. For each region, 11 models were built by combining five clinical parameters and 127 features from: (1) dose images alone;(2-7) fused dose and CT images via wavelet-based fusion using CT weights of 0.2, 0.4, 0.6, and 0.8, gradient transfer fusion, and guided-filtering-based fusion (GFF); (8) fused matrices (sumMat); (9-10) fused features derived via feature averaging (avgFea) and feature concatenation (conFea); and finally, (11) CT images alone. The concordance index (C-index) and Kaplan-Meier curves with log-rank test were used to assess model performance. Results:The fusion models'performance was better than single CT/dose model on both internal and external validation. Models that combined the information from both GTV_nx and GTV_nd regions outperformed the single region model. For internal validation, GTV + RING_3 mm_nxnd GFF model achieved the Chunya Cai and Wenbing Lv contributed equally to this work.

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Section: F I G U R Ementioning

confidence: 80%

“…However, the robust features to some factors may not provide significant information for diagnosis and prognosis. This suggests that we may need to establish fusion model and validate the model on multi‐center external data as in Ref 62 …”

Section: Discussionmentioning

confidence: 99%

Prognostic generalization of multi‐level CT‐dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma

Cai

Chen

et al. 2022

Medical Physics

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“…The median number of criteria with an "adequate" rating per article was 12.5 (IQR, 9-14; range, [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Two articles [12,42] were rated "adequate" in all 17 criteria, whereas 22 articles (22 %) had an "adequate" rating in less than half of the items (≤ 8 of 17 items).…”

Section: Rating Resultsmentioning

confidence: 99%

Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)

Rogasch,

Shi,

Kersting

et al. 2023

Nuklearmedizin

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Aim Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. Methods A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into “adequate” or “inadequate”. The association between the number of “adequate” criteria per article and the date of publication was examined. Results One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated “adequate” was 65% (range: 23–98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an “adequate” rating per article was 12.5 out of 17 (range, 4–17), and this did not increase with later dates of publication (Spearman’s rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated “adequate”. Only 8% of articles published the source code, and 10% made the dataset openly available. Conclusion Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.

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“…The integrated RDMs clinical model significantly improved performance ( p < 0.001) in OS and RFS in both sub‐sites of primary tumours. Introducing parameters relative to tumour heterogeneity, which may account for different uptake patterns (metabolism, hypoxia, and proliferation) or morphological structure (neovascularization) in PET/CT images, Lv et al 40 investigated the ability of context‐aware saliency‐guided PET/CT RDMs in the prediction of outcome for head and neck cancer. In a multi‐centre study, examining 806 HNC patients, saliency‐guided RDMs showed better performances compared to conventional RDMs measured by multivariate Cox analysis scores and evaluated by AUC, Kaplan–Meier curves, log–rank test and ROC curve.…”

Section: Risk Stratification: Survival Predictionmentioning

confidence: 99%

Human papilomaviru‐related oropharyngeal squamous cell carcinoma and radiomics: A new era?

Caprini

D'Agnese

Brennan

et al. 2023

J Oral Pathology Medicine

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Background: The increase of the incidence of human papillomavirus dependent oropharyngeal squamous cell carcinoma is alarming, although we have greatly progressed in the classification and staging of this disease. We now know that human papillomavirus related oropharyngeal squamous cell carcinoma is a sub-type of head and neck squamous cell carcinoma with favourable prognosis and good response to therapy that needs a proper system of classification and staging. Thus, in routine practice it is essential to test patients for the presence of human papillomavirus. The most popular technique to assess human papillomavirus status is immunohistochemistry on biopsy samples with p16, which is an excellent surrogate for high-risk human papillomavirus infection. Another highly sensitive and specific tissue-based technique for the detection of human papillomavirus is RNAscope In situ hybridization that has a prohibitive cost, limiting its use in routine practice. Radiomics is an artificial intelligence based non-invasive method of computational analysis of computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound images. Methods:In this review, we summarise the last findings of radiomics applied to human papillomavirus associated oropharyngeal squamous cell carcinoma.Results: A growing body of evidence suggest that radiomics is able to characterise and detect early relapse after treatment, and enable development of tailored therapy of human papillomavirus positive oropharyngeal squamous cell carcinoma.

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Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer

Cited by 12 publications

References 46 publications

Prognostic generalization of multi‐level CT‐dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma

Prognostic generalization of multi‐level CT‐dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma

Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)

Human papilomaviru‐related oropharyngeal squamous cell carcinoma and radiomics: A new era?

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