A Nomogram for Pretreatment Prediction of Response to Induction Chemotherapy in Locally Advanced Hypopharyngeal Carcinoma

Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer

PURPOSE

By radiomic analysis of the postcontrast CT images, this study aimed to predict locoregional recurrence (LR) of locally advanced oropharyngeal cancer (OPC) and hypopharyngeal cancer (HPC).

METHODS

A total of 192 patients with stage III-IV OPC or HPC from two independent cohort were randomly split into a training cohort with 153 cases and a testing cohort with 39 cases. Only primary tumor mass was manually segmented. Radiomic features were extracted using PyRadiomics, and then the support vector machine was used to build the radiomic model with fivefold cross-validation process in the training data set. For each case, a radiomics score was generated to indicate the probability of LR.

RESULTS

There were 94 patients with LR assigned in the progression group and 98 patients without LR assigned in the stable group. There was no significant difference of TNM staging, treatment strategies and common risk factors between these two groups. For the training data set, the radiomics model to predict LR showed 83.7% accuracy and 0.832 (95% CI 0.72, 0.87) area under the ROC curve (AUC). For the test data set, the accuracy and AUC slightly declined to 79.5% and 0.770 (95% CI 0.64, 0.80), respectively. The sensitivity/specificity of training and test data set for LR prediction were 77.6%/89.6%, and 66.7%/90.5%, respectively.

CONCLUSIONS

The image-based radiomic approach could provide a reliable LR prediction model in locally advanced OPC and HPC. Early identification of those prone to post-treatment recurrence would be helpful for appropriate adjustments to treatment strategies and post-treatment surveillance.

Salvage total laryngectomy for squamous cell carcinoma of the larynx and hypopharynx: Validated prognostic nomograms predicting oncological outcomes

BACKGROUND

Salvage total laryngectomy (STL) is a preferred treatment for patients with residual, recurrent, and second primary squamous cell carcinoma of the larynx/hypopharynx after (chemo)radiation. To individually estimate postoperative oncological outcomes, we designed and validated prognostic nomograms.

METHODS

We used a dataset of 290 patients who underwent STL. Nomograms predicting 2- and 5-year OS, DFS, and DSS were developed, using variables which are identified pre- or postoperatively. The nomograms were externally validated on a dataset of 109 patients.

RESULTS

The nomograms based on postoperative variables performed better than those based on preoperative variables (OS: C = 0.68 vs. 0.64; DFS: C = 0.70 vs. 0.64; DSS: C = 0.74 vs. 0.64). The nomogram predicting DSS based on postoperative variables performed best.

CONCLUSIONS

The presented prognostic nomograms for predicting oncological outcomes in patients who undergo STL are tools which allow for a reliable prognostic assessment.

Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma

RATIONALE AND OBJECTIVES

To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients with head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Radiomics features derived from T2-weighted, pre- and post-contrast-enhanced T1-weighted MRI sequences, clinical data, and RNA sequencing data of 150 patients with HNSCC were included in the study. Analysis of variance or recursive feature elimination was used to reduce radiomics features. Three RBML classifiers were developed to distinguish non-responders from responders. Weighted correlation network analysis (WGCNA) was performed to identify the correlation between clinical data or radiomics features and molecular features; subsequently, protein interaction and functional enrichment analyses were performed. The predictive performance of the radiogenomics model integrating significant clinical variables, RBML classifiers, and molecular features was evaluated using receiver operating characteristic curve analysis.

RESULTS

Five radiomics features and two conventional MRI findings significantly stratified HNSCC patients into responders and non-responders. On WGCNA analysis, 809 genes showed a significant correlation with two radiomics features. Functional enrichment analysis suggested that our proposed radiomics features could reflect the T cell-mediated immune response and immune infiltration of HNSCC. The radiogenomics model showed the highest area under the curve (0.88[95%CI 0.75-0.96]) for predicting IC response, which was better than MRI findings(p = 0.0407) or molecular features(p = 0.004) alone, but showed no significant difference with that of RBML model (p = 0.2254) in test cohort.

CONCLUSION

Merging imaging phenotypes with transcriptomic data improved the prediction of IC response in HNSCC.

CT-based radiomics signature analysis for evaluation of response to induction chemotherapy and progression-free survival in locally advanced hypopharyngeal carcinoma

OBJECTIVES

To establish and validate a CT radiomics model for prediction of induction chemotherapy (IC) response and progression-free survival (PFS) among patients with locally advanced hypopharyngeal carcinoma (LAHC).

METHODS

One hundred twelve patients with LAHC (78 in training cohort and 34 in validation cohort) who underwent contrast-enhanced CT (CECT) scans prior to IC were enrolled. Least absolute shrinkage and selection operator (LASSO) was used to select the crucial radiomic features in the training cohort. Radiomics signature and clinical data were used to build a radiomics nomogram to predict individual response to IC. Kaplan-Meier analysis and log-rank test were used to evaluate ability of radiomics signature in progression-free survival risk stratification.

RESULTS

The radiomics signature consisted of 6 selected features from the arterial and venous phases of CECT images and demonstrated good performance in predicting the IC response in both two cohorts. The radiomics nomogram showed good discriminative performance, and the C-index of nomogram was 0.899 (95% confidence interval (CI), 0.831-0.967) and 0.775 (95% CI, 0.591-0.959) in the training and validation cohorts, respectively. Survival analysis indicated that low-risk and high-risk groups defined by the value of radiomics signature had significant difference in PFS (3-year PFS 66.4% vs 29.7%, p < 0.001).

CONCLUSIONS

Multiparametric CT-based radiomics model could be useful for predicting treatment response and PFS in patients with LAHC who underwent IC.

KEY POINTS

• CT radiomics can predict IC response and progression-free survival in hypopharyngeal carcinoma. • We combined significant radiomics signature with clinical predictors to establish a nomogram to predict individual response to IC. • Radiomics signature could divide patients into the high-risk and low-risk groups based on the PFS.

Computed tomography-based radiomics signature as a pretreatment predictor of progression-free survival in locally advanced hypopharyngeal carcinoma with a different response to induction chemotherapy

PURPOSE

To establish and validate a radiomics signature for stratifying the risk of progression-free survival (PFS) in patients with locally advanced hypopharyngeal carcinoma (LAHC) undergoing induction chemotherapy (IC).

METHODS

We extracted radiomics features from baseline contrast-enhanced computed tomography (CECT) images. We enrolled 112 LAHC patients (78 in the training cohort and 34 in the validation cohort). We used cox regression model and random survival forests variable hunting (RSFVH) algorithm for feature selection and radiomics signature building. The radiomics signature was established in the training cohort and tested in the validation cohort. We used the Kaplan-Meier analysis and log-rank test to evaluate the ability of radiomics signature in PFS risk stratification among patients with different IC responses and constructed a radiomics nomogram to predict individual PFS risk.

RESULTS

The radiomics signature performed well in stratifying patients into highrisk and low-risk groups of progression in both the training and validation cohorts. The radiomics nomogram showed good discriminative ability for predicting PFS. Survival outcome analysis of subsets indicated that the radiomics signature performed well in stratifying the risk of PFS in patients with LAHC with different IC responses.

CONCLUSIONS

The radiomics signature was a pretreatment predictor for PFS in patients with LAHC who exhibited different responses to IC.

Matched-Pair Analysis of Survival in the Patients with Advanced Laryngeal and Hypopharyngeal Squamous Cell Carcinoma Treated with Induction Chemotherapy Plus Chemo-Radiation or Total Laryngectomy

Simple Summary

There are no randomized studies comparing organ-preservation protocols with chemo-selection to the classical total laryngectomy plus (chemo)radiotherapy. Thus, we performed a matched-pair analysis to compare these two treatments with respect to locoregional control, disease-specific survival (DSS), and overall survival (OS) in patients with locally advanced squamous cell carcinoma of the larynx and hypopharynx. The results did not show differences between the two treatments for patients with T3/T4a larynx and T2–T4a hypopharynx cancer with respect to OS and DSS, locoregional control, and metastasis-free survival.

Abstract

Background: We performed a comparative analysis between an organ-preservation protocol and surgery followed by radiotherapy in patients with locally advanced squamous cell carcinoma of the larynx and hypopharynx; Methods: 60 previously untreated patients who were treated with induction chemotherapy followed by chemoradiotherapy in responders were compared with a control group of 60 patients treated with up-front surgery. Both groups were statistically comparable, according to the subsite, TNM (tumor-node-metastasis) stage, age, and sex; Results: Mean age was 58 years and 92% were male. No significant statistical difference was observed for overall survival (OS) (HR 0.75; 95% CI 0.48–1.18; P = 0.22) and disease-specific survival (DSS) (HR 0.98; 95% CI 0.52–1.83, P = 0.96). Also, there was no significant difference for recurrence-free survival (HR 0.931; 95% CI 0.57–1.71; P = 0.81), metastases-free survival (HR 2.23; 95% CI 0.67–7.41; P = 0.19), and the appearance of second primary tumors (HR 1.22; 95% CI 0.51–2.88; P = 0.64); Conclusions: The results of the organ-preservation approach did not appear inferior to those of surgery plus (chemo)radiotherapy for patients with T3/T4a larynx and T2–T4a hypopharynx cancer with respect to OS and DSS, locoregional control and metastases-free survival.