An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: a multi-cohort study

Chen, Juan; Lu, Shanhong; Mao, Yun; Tan, Lei; Li, Guo; Gao, Yan; Tan, Pingqing; Huang, Donghai; Zhang, Xin; Qiu, Yuanzheng; Liu, Yong

doi:10.1007/s00330-021-08292-z

Cited by 17 publications

(13 citation statements)

References 32 publications

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“…Our study confirms and expands previous data on the prognostic utility of radiomic features in patients with hypopharyngeal cancer [ 17 ]. However, prior studies were conducted with heterogeneous samples in terms of disease stage, with the majority of participants being treated with surgical excision [ 17 ]. In the current investigation, we specifically focused on patients with advanced-stage hypopharyngeal cancer who had undergone CCRT.…”

Section: Discussionsupporting

confidence: 91%

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy

Siow

Yeh

Lin

et al. 2022

Cancers

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A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88–59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator–Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558–0.591) and PFS (iAUC = 0.625, 95% CI = 0.600–0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225–0.334] and 0.293 [95% CI = 0.232–0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.

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Section: Discussionsupporting

confidence: 91%

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy

Siow

Yeh

Lin

et al. 2022

Cancers

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“…Twelve studies, all of them retrospective, were subjected to qualitative analysis [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. The demographic characteristics of patients from these studies are summarized in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

“…Excluding one study that was based on the same sample as another study, we included a total of 889 patients who underwent treatment for HPSCC. Considering the 5 studies that provided data on age, the median age was 51 years old [ 23 , 24 , 25 , 26 , 27 ]. A total of 8 studies described patients by gender, yielding 645 (94.6%) males and 37 (5.4%) females [ 16 , 18 , 19 , 20 , 23 , 24 , 26 , 27 ].…”

Section: Resultsmentioning

confidence: 99%

Radiomics in Hypopharyngeal Cancer Management: A State-of-the-Art Review

et al. 2023

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(1) Background: Hypopharyngeal squamous cell carcinomas usually present with locally advanced disease and a correspondingly poor prognosis. Currently, efforts are being made to improve tumor characterization and provide insightful information for outcome prediction. Radiomics is an emerging area of study that involves the conversion of medical images into mineable data; these data are then used to extract quantitative features based on shape, intensity, texture, and other parameters; (2) Methods: A systematic review of the peer-reviewed literature was conducted; (3) Results: A total of 437 manuscripts were identified. Fifteen manuscripts met the inclusion criteria. The main targets described were the evaluation of textural features to determine tumor-programmed death-ligand 1 expression; a surrogate for microvessel density and heterogeneity of perfusion; patient stratification into groups at high and low risk of progression; prediction of early recurrence, 1-year locoregional failure and survival outcome, including progression-free survival and overall survival, in patients with locally advanced HPSCC; thyroid cartilage invasion, early disease progression, recurrence, induction chemotherapy response, treatment response, and prognosis; and (4) Conclusions: our findings suggest that radiomics represents a potentially useful tool in the diagnostic workup as well as during the treatment and follow-up of patients with HPSCC. Large prospective studies are essential to validate this technology in these patients.

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“…The features included in our radiomics model differed from those used by Ligero et al [28], which may be related to differences in the types of images used. In addition to the original images, we also used wavelet-transformed images for feature extraction in our study, as these can provide more information concerning intra-tumor heterogeneity [29].…”

Section: Discussionmentioning

confidence: 99%

Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers

Bai

Zheng

et al. 2022

BMC Cancer

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Background Immune checkpoint inhibitors (ICIs) represent an approved treatment for various cancers; however, only a small proportion of the population is responsive to such treatment. We aimed to develop and validate a plain CT-based tool for predicting the response to ICI treatment among cancer patients. Methods Data for patients with solid cancers treated with ICIs at two centers from October 2019 to October 2021 were randomly divided into training and validation sets. Radiomic features were extracted from pretreatment CT images of the tumor of interest. After feature selection, a radiomics signature was constructed based on the least absolute shrinkage and selection operator regression model, and the signature and clinical factors were incorporated into a radiomics nomogram. Model performance was evaluated using the training and validation sets. The Kaplan–Meier method was used to visualize associations with survival. Results Data for 122 and 30 patients were included in the training and validation sets, respectively. Both the radiomics signature (radscore) and nomogram exhibited good discrimination of response status, with areas under the curve (AUC) of 0.790 and 0.814 for the training set and 0.831 and 0.847 for the validation set, respectively. The calibration evaluation indicated goodness-of-fit for both models, while the decision curves indicated that clinical application was favorable. Both models were associated with the overall survival of patients in the validation set. Conclusions We developed a radiomics model for early prediction of the response to ICI treatment. This model may aid in identifying the patients most likely to benefit from immunotherapy.

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An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: a multi-cohort study

Cited by 17 publications

References 32 publications

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy

Radiomics in Hypopharyngeal Cancer Management: A State-of-the-Art Review

Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers

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