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Background: The clinical significance of lymph node (LN) status determined by preoperative 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) has not been investigated in patients with locally advanced esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (NCRT) followed by surgery (trimodal therapy). Methods: This study reviewed 132 consecutive patients with ESCC who had been preoperatively evaluated using FDG-PET before and after NCRT to analyze associations among LN status according to PET findings, pathologic LN metastasis, and prognosis of ESCC after trimodal therapy. Results: Lymph nodes that were PET-positive both before and after NCRT comprised significant predictive markers of pathologic LN metastasis in station-by-station analyses (sensitivity, specificity, and accuracy respectively 41.7%, 95.0%, and 92.7% before, and 12.0%, 99.4%, and 95.6% after NCRT; both p < 0.0001). The numbers of LNs evaluated using PET before and after NCRT were significantly associated with those of pathologic metastatic LNs. Uni- and multivariable analyses selected LN status determined by PET before NCRT as a significant independent predictor of both recurrence-free [LN-negative vs LN-positive: hazard ratio (HR) 1.90; 95% confidence interval (CI) 1.02–3.23; p = 0.045] and overall survival (HR 2.62; 95% CI 1.29–5.30; p = 0.01). Conclusions: The status of LN determined by preoperative FDG-PET is significantly associated with pathologic LN status and the prognosis of ESCC with trimodal therapy. Thus, FDG-PET is a useful diagnostic tool for preoperative prediction of pathologic LN metastasis and survival among patients with ESCC. [ABSTRACT FROM AUTHOR]

Multi-dithienylethene arrays were synthesized by connecting their moieties with ethynylene bonds. The photocyclization quantum yields increased by increasing the number of dithienylethene units in the multidithienylethene arrays.

[Display omitted] • This work proposed a synthetic multi-contrast MRI image using a GAN model for cervical cancer patients. • The GAN model was created with open-access data, which was evaluated with other institutions' data. • The synthesized MRI image was used for the prediction of the recurrence of cervical cancer patients. • The prediction performance of the synthesized MRI image was equivalent to that of the real MRI image. • The prediction performance can be improved by combining the scanned and synthesized MRI images. To developa generative adversarial network-based image synthesis (IS) model capable of predicting recurrence after radiotherapy in locally advanced cervical cancer. T1- and T2-weighted magnetic resonance (MR) images were synthesised using cohorts from The Cancer Imaging Archive. The results of the IS model were evaluated by comparison with real MR images. The trained IS model synthesised the MR images for the input images in the radiomics analysis dataset. A prediction model for recurrence after radiotherapy was constructed using radiomics features from real and synthesized MR images. The accuracy, specificity, sensitivity, and receiver operating characteristic curves were evaluated. Using the LASSO regression analysis, seven and six features were extracted from the real and synthesized T1-weighted MR images, respectively, and five and seven features were extracted from the real and synthesized T2-weighted MR images, respectively. The average prediction accuracies of the five cross-validations were 78.9% and 74.3% for the real and synthesized T1-weighted MR images and 81.9% and 81.6% for the real and synthesized T2-weighted MR images, respectively. The average prediction accuracies for combined model of the real T1-weighted and real T2-weighted MR images was 90.3%, that of the real T1-weighted and synthesized T2-weighted MR images was 90.6%, and that of the real T1-weighted and synthesized T2-weighted MR images was 83.8%. The prediction performance of the synthesized MR image was equivalent to that of the real MR image. The prediction performance can be improved by combining the scanned and synthesized MR images. [ABSTRACT FROM AUTHOR]

Purpose: Intensity‐modulated radiation therapy (IMRT) utilizes many small fields for producing a uniform dose distribution. Therefore, there are many field junctions in the target region, and resulting dose uncertainties are accumulated. However, such accumulation of the dose uncertainty has not been implemented in the current practice of IMRT dose verification. The purpose of this study is to develop a method to predict the gamma passing rate (GPR) using a dose uncertainty accumulation model. Methods: Thirty‐three intensity‐modulated (IM) beams for head‐and‐neck cases with step‐and‐shoot techniques were used in this study. The treatment plan was created using the XiO treatment planning system (TPS). The IM beam was produced by the ONCOR Impression Plus linear accelerator. MapCHECK was used to measure the dose distribution. The distribution of a dose uncertainty potential (DUP) was generated by in‐house software that accumulated field shapes weighted by a segmental monitor unit, followed by Gaussian folding. The width of the Gaussian was determined from the width of the lateral penumbra. The dose difference between the calculated and measured doses was compared with the estimated DUP at each point. The GPR of each beam was predicted for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances by its own DUP histogram and a GPR‐vs‐DUP correlation of other beams using the leave‐one‐out cross‐validation method. The predicted GPR was compared with the measured GPR to evaluate the performance of this prediction method. The criteria for the predicted GPR corresponding to a measured GPR ≥ 90% were estimated to examine the feasibility of estimating the measured GPR by this GPR prediction method. Results: The DUP was confirmed to have proportionality to the standard deviation (SD) of the dose difference. The SDs of the difference between the measured and predicted GPRs were 3.1, 1.7, and 1.4% for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances, respectively. The criteria of the predicted GPR corresponding to the measured GPR ≥ 90% were 94.1 and 95.0% with confidence levels of 99 and 99.9%, respectively. Conclusion: In this study, we confirmed the good proportionality between the dose difference and the estimated DUP. The results showed a feasibility to predict the dose difference from DUP as estimated by a DUP accumulation model. The predicted GPR developed in this study showed good accuracy for planar dose distributions of head and neck IMRT. The prediction method developed in this study is considered to be feasible as a substitute for the current practice of measurement‐based verification of the dose distribution with gamma analysis. [ABSTRACT FROM AUTHOR]

Background: Intensive trimodality therapy is needed for locally advanced esophageal squamous cell carcinoma (ESCC). However, some patients develop recurrence and die of cancer even after trimodality therapy.Methods: We evaluated prognostic factors based on data from 125 patients with ESCC who underwent neoadjuvant chemoradiotherapy (NCRT) comprising concurrent chemotherapy and 40 Gy of radiation, followed by curative-intent esophagectomy.Results: Thirty-four (27.2%) patients achieved a pathological complete response (pCR) after NCRT. The 5-year recurrence-free (RFS) and overall survival (OS) rates of all patients were 49.2 and 52.9%, respectively, and were significantly better for patients with pCR than without pCR (p = 0.01 and 0.02, respectively). Univariate and multivariate analyses selected performance status [PS 0 vs. 1: hazard ratio (HR) 2.05; 95% confidence interval (CI) 1.30-4.84; p = 0.01] and ypN (0 vs. 1: HR 2.33; 95% CI 1.12-4.84; p = 0.02; 0 vs. 2/3: HR 3.73; 95% CI 1.68-8.28; p = 0.001) as independent covariates for RFS. Furthermore, PS (0 vs. 1; HR 2.94; 95% CI 1.51-5.72; p = 0.002) and ypN (0 vs. 1; HR 2.26; 95% CI 1.09-4.69; p = 0.03; 0 vs. 2/3: HR 3.90; 95% CI 1.79-8.48; p = 0.001) were also independent covariates for OS.Conclusions: Performance status 1 and ypN+ were significantly associated with a poor prognosis after trimodality therapy for ESCC. [ABSTRACT FROM AUTHOR]