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.

Evaluation of a combined respiratory-gating system comprising the TrueBeam linear accelerator and a new real-time tumor-tracking radiotherapy system: a preliminary study.

A combined system comprising the TrueBeam linear accelerator and a new real-time, tumor-tracking radiotherapy system, SyncTraX, was installed in our institution. The goals of this study were to assess the capability of SyncTraX in measuring the position of a fiducial marker using color fluoroscopic images, and to evaluate the dosimetric and geometric accuracy of respiratory-gated radiotherapy using this combined system for the simple geometry. For the fundamental evaluation of respiratory-gated radiotherapy using SyncTraX, the following were performed:1) determination of dosimetric and positional characteristics of sinusoidal patterns using a motor-driven base for several gating windows; 2) measurement of time delay using an oscilloscope; 3) positional verification of sinusoidal patterns and the pattern in the case of a lung cancer patient; 4) measurement of the half-value layer (HVL in mm AL), effective kVp, and air kerma, using a solid-state detector for each fluoroscopic condition, to determine the patient dose. The dose profile in a moving phantom with gated radiotherapy having a gating window ≤ 4 mm was in good agreement with that under static conditions for each photon beam. The total time delay between TrueBeam and SyncTraX was < 227 ms for each photon beam. The mean of the positional tracking error was < 0.4 mm for sinusoidal patterns and for the pattern in the case of a lung cancer patient. The air-kerma rates from one fluoroscopy direction were 1.93 ± 0.01, 2.86 ± 0.01, 3.92 ± 0.04, 5.28 ± 0.03, and 6.60 ± 0.05 mGy/min for 70, 80, 90, 100, and 110 kV X-ray beams at 80 mA, respectively. The combined system comprising TrueBeam and SyncTraX could track the motion of the fiducial marker and control radiation delivery with reasonable accuracy; therefore, this system provides significant dosimetric improvement. However, patient exposure dose from fluoroscopy was not clinically negligible.

[Construction and usefulness of the dose management system using DICOM radiation dose structured report (Dose SR) in angiography].

The International Commission on Radiological Protection recommends diagnostic reference levels (DRL) in each radiological examination for justification and optimization of patients' dose in medicine. The aim of our study was to propose the dose management system by utilizing dose information in diagnostic X-ray radiation dose structured report (Dose SR) in The Digital Imaging and Communications in Medicine to optimize radiation dose in institutions. Our dose management system is able to organize dose information obtained from various angiography systems and CTs. It is possible to provide this information to operators for justification and optimization of patient dose. Our system would be useful for the estimation of organ dose and could be used for the determination of local DRL (LDRL) for each radiological practice. In addition, the optimization became possible to compare LDRL with national DRL.

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.

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.

Patient-specific radiotherapy quality assurance for estimating actual treatment dose.

This study aimed to evaluate the optimal method for planning computed tomography (CT) for prostate cancer radiotherapy to avoid a dose difference of ≥3% between the actual and planned treatments using multiple acquisition planning CT (MPCT). We calculated the 3-dimensional (3D) displacement error between the pelvic bone and matching fiducial marker on MPCT and cone-beam CT scans of 25 patients who underwent prostate volumetric-modulated arc therapy for prostate cancer. The correlation of the 3D displacement error and the dose difference between planned and actual treatments was calculated using least squares second-order polynomial model. The 3D displacement error showed a moderate correlation with differences between planned and accumulated treatment doses (r = 0.587, p < 0.0001). Moreover, the improvement rate of the minimum 3D displacement error showed a strong correlation with the relative error between each MPCT image (r = 0.793, p < 0.0001). Significant differences were observed between planned and actual treatment doses (p < 0.0001) in the relative 3D displacement errors of <1 mm, 1 to 3 mm, and >3 mm. The 3D displacement error on MPCT (as the selection estimation index for optimal planning CT) is useful for monitoring patient-specific intensity-modulated radiation therapy quality assurance. This new method allows to estimate dose differences from the planned dose before commencing treatment, thereby ensuring high-quality therapy. As radiotherapy quality is critical for patient outcome, these findings may contribute to better management of prostate cancer.

Optimizing planning CT using past CT images for prostate cancer volumetric modulated arc therapy.

This study aimed to evaluate a new method to optimize planning computed tomography (CT) using three-dimensional (3D) displacement error between the planning and diagnosed past CT scans. Thirty-two patients undergoing volumetric modulated arc therapy for prostate cancer were evaluated for a 3D displacement error between bone- and prostate-matching spatial coordinates using multiple acquisition planning CT (MPCT) scans. Each MPCT image and a past CT image were used to perform rigid image registration (RIR) and deformable image registration (DIR), and the 3D displacement error was calculated. Correlations of the 3D displacement error in each MPCT scan and between the MPCT and past CT were evaluated based on RIR and DIR, respectively. The 3D displacement error in the MPCT images exhibited moderate correlation with the 3D displacement error between MPCT and past CT for both RIR (adjusted r2 = 0.495) and DIR (adjusted r2 = 0.398). In the correlation analysis between MPCT and past CT, image pairs with 3D displacement errors ≥ 6 mm were significantly different from those with errors < 6 mm (p < 0.0001). Past CT images were different from the planning CT images, which can be attributed to setup tools, flat-top plates, and physical differences due to the presence or absence of urine as well as prescription effects. The relationship between bone and prostate exhibited small deviations between the planning and past CT regardless of pretreatment. The prostate, which only has a slight effect on the displacement between it and bladder volume, was covered with a stiff pelvic bone. As a result, MPCT images exhibited correlations with past CT images of various difference states such as body positions. Finally, large 3D displacement errors in prostate position were caused by pelvic tension and stress, which can be detected using diagnosed past CT images instead of requiring MPCT scans. By comparing past and planning CT images, the random displacement error in the planning CT scan can be avoided by evaluating 3D displacement errors. The new method using the past CT images can estimate the displacement error of the prostate during the treatment period with 1 plan CT scan only, and it helps improve the treatment accuracy.

Evaluation of the correlation between prostatic displacement and rectal deformation using the Dice similarity coefficient of the rectum.

To estimate the relationship between the three-dimensional (3D) displacement error of the prostate and rectal deformation for reduction of deviation between the planned and treatment dose, using multiple acquisition planning CT (MPCT) and the Dice similarity coefficient (DSC) for rectal deformation for treatment of patients with prostate cancer. The 3D displacement error between the pelvic bone and a matching fiducial marker was calculated using MPCT in 24 patients who underwent prostate volumetric-modulated arc therapy for prostate cancer. We calculated the 3D displacement error between the pelvic bone and a matching fiducial marker on MPCT. The correlation of the 3D displacement error with the DSC of the rectum, calculated from MPCT images, was evaluated based on deformable image registration. The 3D displacement error of the prostate showed a slight correlation between MPCT and cone-beam computed tomography (adjusted r2 = 0.241). The 3D displacement error, based on the pelvic bone and a fiducial marker on MPCT images, showed a moderate correlation with the DSC of the rectum (adjusted r2 = 0.645) and was improved by a mean of 3.94 mm, based on MPCT, during the treatment period. The 3D displacement error on MPCT correlates with the 3D displacement error of daily cone-beam computed tomography; optimal selection of MPCT can potentially facilitate on-board setup of prostate patients to enable more accurate radiotherapy. The advance information of the 3D displacement error and rectal deformation is useful for optimal planning CT that can minimize the deviation between the planned dose and the treatment dose in patients receiving treatment for prostate cancer.