Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model.

OBJECTIVES: To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features. METHODS: The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers. RESULTS: A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers. CONCLUSION: The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients. CLINICAL RELEVANCE STATEMENT: The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed. KEY POINTS: • Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.

Influence of the cytochrome P450 2D6 *10/*10 genotype on the pharmacokinetics of paroxetine in Japanese patients with major depressive disorder: a population pharmacokinetic analysis.

OBJECTIVE: Although the reduced function of the cytochrome P450 2D6*10 (CYP2D6*10) allele is common among Asian populations, existing evidence does not support paroxetine therapy adjustments for patients who have the CYP2D6*10 allele. In this study, we attempted to evaluate the degree of the impact of different CYP2D6 genotypes on the pharmacokinetic (PK) variability of paroxetine in a Japanese population using a population PK approach. METHODS: This retrospective study included 179 Japanese patients with major depressive disorder who were being treated with paroxetine. CYP2D6*1, *2, *5, *10, and *41 polymorphisms were observed. A total of 306 steady-state concentrations for paroxetine were collected from the patients. A nonlinear mixed-effects model identified the apparent Michaelis-Menten constant (Km) and the maximum velocity (Vmax) of paroxetine; the covariates included CYP2D6 genotypes, patient age, body weight, sex, and daily paroxetine dose. RESULTS: The allele frequencies of CYP2D6*1, *2, *5, *10, and *41 were 39.4, 14.5, 4.5, 41.1, and 0.6%, respectively. There was no poor metabolizer who had two nonfunctional CYP2D6*5 alleles. A one-compartment model showed that the apparent Km value was decreased by 20.6% in patients with the CYP2D6*10/*10 genotype in comparison with the other CYP2D6 genotypes. Female sex also influenced the apparent Km values. No PK parameters were affected by the presence of one CYP2D6*5 allele. CONCLUSION: Unexpectedly, elimination was accelerated in individuals with the CYP2D6*10/*10 genotype. Our results show that the presence of one CYP2D6*5 allele or that of any CYP2D6*10 allele may have no major effect on paroxetine PKs in the steady state.

A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer.

OBJECTIVES: To propose a nomogram-based survival prediction model for esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy using pretreatment computed tomography (CT), positron emission tomography (PET) radiomics and dosiomics features, and common clinical factors. METHODS: Radiomics and dosiomics features were extracted from CT and PET images and dose distribution from 2 institutions. The least absolute shrinkage and selection operator (LASSO) with logistic regression was used to select radiomics and dosiomics features by calculating the radiomics and dosiomics scores (Rad-score and Dos-score), respectively, in the training model. The model was trained in 81 patients and validated in 35 patients at Center 1 using 10-fold cross validation. The model was externally tested in 26 patients at Center 2. The predictive clinical factors, Rad-score, and Dos-score were identified to develop a nomogram model. RESULTS: Using LASSO Cox regression, 13, 11, and 19 CT, PET-based radiomics, and dosiomics features, respectively, were selected. The clinical factors T-stage, N-stage, and clinical stage were selected as significant prognostic factors by univariate Cox regression. In the external validation cohort, the C-index of the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were 0.74, 0.82, and 0.92, respectively. Significant differences in overall survival (OS) in the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were observed between the high- and low-risk groups (P = 0.019, 0.038, and 0.014, respectively). CONCLUSION: The dosiomics features have a better predicter for OS than CT- and PET-based radiomics features in ESCC treated with radiotherapy. CLINICAL RELEVANCE STATEMENT: The current study predicted the overall survival for esophageal squamous cell carcinoma patients treated with definitive chemoradiotherapy. The dosiomics features have a better predicter for overall survival than CT- and PET-based radiomics features.

Targeting Anterior Commissure Involvement with Hyperfractionated Radiotherapy for T1-T2 Squamous Cell Carcinoma of the Glottic Larynx.

Anterior commissure is involved in about 20% of early-stage glottic squamous cell carcinomas (EGSCCs). Treatment outcomes and prognostic factors for EGSCC with anterior commissure involvement (ACI) were evaluated by focusing on hyperfractionated radiotherapy (74.4 Gy in 62 fractions). One-hundred and fifty-three patients with T1-T2 EGSCC were included in this study. The median total doses for T1a, T1b, and T2 were 66, 74.4, and 74.4 Gy, respectively. Overall, 49 (32%) patients had T1a, 38 (25%) had T1b, and 66 (43%) had T2 disease. The median treatment duration was 46 days. The median follow-up duration was 5.1 years. The 10-year overall and cause-specific survival rates were 72% and 97%, respectively. The 10-year local control rates were 94% for T1a, 88% for T1b, and 81% for T2 disease. Local control rates in patients with ACI were slightly better than those in patients without ACI with T1a and T1b diseases; however, the difference was not significant. The 10-year laryngeal preservation rate was 96%. Six patients experienced grade 3 mucositis, and four patients had grade 3 dermatitis. Hyperfractionated radiotherapy was effective for T1 disease with ACI, but insufficient for T2 disease with ACI. Our treatment strategy resulted in excellent laryngeal preservation.

Prognostic Value of Radiomics Analysis of Skeletal Muscle After Radical Irradiation of Esophageal Cancer.

BACKGROUND/AIM: Sarcopenia is an independent survival predictor in several tumor types. Computed tomography (CT) is the standard measurement for body composition assessment. Radiomics analysis of CT images allows for the precise evaluation of skeletal muscles. This study aimed to construct a prognostic survival model for patients with esophageal cancer who underwent radical irradiation using skeletal muscle radiomics. PATIENTS AND METHODS: We retrospectively identified patients with esophageal cancer who underwent radical irradiation at our institution between April 2008 and December 2017. Skeletal muscle radiomics were extracted from an axial pretreatment CT at the third lumbar vertebral level. The prediction model was constructed using machine learning coupled with the least absolute shrinkage and selection operator (LASSO). The predictive nomogram model comprised clinical factors with radiomic features. Three prediction models were created: clinical, radiomics, and combined. RESULTS: Ninety-eight patients with 98 esophageal cancers were enrolled in this study. The median observation period was 57.5 months (range=1-98 months). Thirty-five radiomics features were selected by LASSO analysis, and a prediction model was constructed using training and validation data. The average of the accuracy, specificity, sensitivity, and area under the concentration-time curve for predicting survival in esophageal cancer in the combined model were 75%, 92%, and 0.86, respectively. The C-indices of the clinical, radiomics, and combined models were 0.76, 0.80, and 0.88, respectively. CONCLUSION: A prediction model with skeletal muscle radiomics and clinical data might help determine survival outcomes in patients with esophageal cancer treated with radical radiotherapy.