Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

Abstract

ObjectivesTo assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma.MethodsTwenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance.ResultsFifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914).ConclusionsDCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.

Pretreatment radiomics power in evaluating neoadjuvant chemotherapy response and outcome for patients with head and neck squamous cell carcinoma: a systematic review and meta-analysis

“…Included studies and quality assessment A total of 375 articles were retrieved, 325 articles were excluded at the initial assessment screening the topics and abstracts, and 50 full text articles were further screened. Finally, 16 identi ed relevant records [9,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], 8238 patients were included in this meta-analysis published from January 2019 to June 2022. Among them, one article [27] was included twice, because it made predictive analysis on different chemotherapy schemes.…”

“…Since the data used in external validation are obtained from a separate database, the external validation model is more reliable and accurate than that of the internal validation model. Eleven [23,25,27,[29][30][31][32][33][34][35][36] articles included in the meta-analysis used internal validation, and four [9,22,24,26] articles used external validation. External validation is necessary in validating the model's generality and ensuring the randomness of patient selection and the homogeneity of the clinical characteristics [44].…”

New Challenges in Evaluating Outcomes after Immunotherapy in Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma

“…For instance, the potential for airway obstruction, surgical resection, or radiotherapy to the site may alter the course of treatment. Unfortunately, common dimensional criteria, such as multiparametric MRI (e.g., tumor volume and apparent diffusion coefficient), are of limited help [12].…”