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Objectives: Low-dose-rate brachytherapy (LDR-BT) with 198Au grains and 192Ir pins is an essential treatment option for oral cancer due to its high rate of local control and low invasiveness. However, the radiation exposure of medical radiation workers is concerning. Thus, we aimed to determine the radiation dose delivered to medical radiation workers during LDR-BT using 198Au grains and 192Ir pins for oral cancer. Methods: Thirty-two patients with oral cancer underwent 198Au grain interstitial LDR-BT between June 2016 and May 2023, and 23 patients with tongue cancer underwent 192Ir pin interstitial LDR-BT between March 2015 and November 2017 at our hospital. Dosimetry was performed by attaching a dosimeter to the chest pocket of the operator and assistant during 198Au grain or 192Ir pin LDR-BT. Since the operator also loads 198Au grains into the implantation device, the operator's radiation dose includes the dose received during this preparation. Results: Mean radiation doses of the operators with 198Au grain and 192Ir pin LDR-BT were 165.8 and 211.2 μSv, respectively. Statistically significant differences between the radioactive sources of 198Au grain and 192Ir pin LDR-BT were observed (p = 0.0459). The mean radiation doses of the assistants with 198Au grain and 192Ir pin LDR-BT were 92.0 and 162.0 μSv, respectively. Statistically significant differences were observed between the radioactive sources of 198Au grains and 192Ir pin LDR-BT (p = 0.0003). Conclusions: Regarding radioactive source differences, 192Ir pin LDR-BT resulted in higher doses delivered to medical radiation workers than 198Au grain LDR-BT. [ABSTRACT FROM AUTHOR]

Background: Pathological lymph node metastasis (LNM) following multimodal therapy is an important indicator of poor prognosis in patients with esophageal cancer. However, a significant number of patients without LNM are still at high risk for recurrence. Methods: We assessed prognostic factors in 143 patients without pathological LNM who were diagnosed with locally advanced esophageal squamous cell carcinoma (ESCC) and underwent neoadjuvant chemotherapy (NAC) or chemoradiotherapy (NACRT), followed by surgery. Results: Using univariate and multivariate analyses of recurrence‐free survival, carcinoembryonic antigen (CEA) levels (hazard ratio [HR]: 2.17, 95% confidence interval [CI]: 1.12–4.23, and p = 0.02) and neutrophil‐to‐lymphocyte ratio (NLR) (HR: 1.22, 95% CI: 1.04–1.43, and p = 0.02) were significant independent covariates. Furthermore, pretherapeutic LNM (HR: 1.94, 95% CI: 1.003–3.76, and p = 0.049), NACRT (HR: 3.29, 95% CI: 1.30–8.33, and p = 0.01), poorly differentiated tumors (HR: 2.52, 95% CI: 1.28–4.98, and p = 0.01), and lymphovascular invasion (LVI) (HR: 2.78, 95% CI: 1.27–6.09, and p = 0.01) were also significant independent covariates. The recurrence rates among patients with 0/1, 2, 3, and 4/5 poor prognostic factors were significantly different (5.0%, 25.0%, 35.7%, and 53.8%, respectively; p = 0.001); the survival rates were stratified among these prognostic groups. Conclusions: Pretherapeutic CEA and NLR levels, pretherapeutic LNM, NACRT, poorly differentiated tumors, and LVI were significantly correlated with survivals in patients without pathological LNM after neoadjuvant therapy and surgery. Postoperative therapy should be considered in patients with ESCC with several indicators of recurrence, even in those without pathological LNM who underwent surgery following neoadjuvant therapy. [ABSTRACT FROM AUTHOR]

Objective This study evaluated the prognostic impact of the quality of dose distribution using dosiomics in patients with prostate cancer, stratified by pretreatment prostate-specific antigen (PSA) levels and Gleason grade (GG) group. Methods A total of 721 patients (Japanese Foundation for Cancer Research [JFCR] cohort: N  = 489 and Tokyo Radiation Oncology Clinic [TROC] cohort: N  = 232) with localized prostate cancer treated by intensity-modulated radiation therapy were enrolled. Two predictive dosiomic features for biochemical recurrence (BCR) were selected and patients were divided into certain groups stratified by pretreatment PSA levels and GG. Freedom from biochemical failure (FFBF) was estimated using the Kaplan–Meier method based on each dosiomic feature and univariate discrimination was evaluated using the log-rank test. As an exploratory analysis, a dosiomics hazard (DH) score was developed and its prognostic power for BCR was examined. Results The dosiomic feature extracted from planning target volume (PTV) significantly distinguished the high- and low-risk groups in patients with PSA levels >10 ng/mL (7-year FFBF: 86.7% vs 76.1%, P  <  .01), GG 4 (92.2% vs 76.9%, P  <  .01), and GG 5 (83.1% vs 77.8%, P  =  .04). The DH score showed significant association with BCR (hazard score: 2.04; 95% confidence interval: 1.38-3.01; P  <  .001). Conclusion The quality of planned dose distribution on PTV may affect the prognosis of patients with poor prognostic factors, such as PSA levels >10 ng/mL and higher GGs. Advances in knowledge The effects of planned dose distribution on prognosis differ depending on the patient's clinical background. [ABSTRACT FROM AUTHOR]

Background: Predicting models of the gamma passing rate (GPR) have been studied to substitute the measurement‐based gamma analysis. Since these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array, it has been difficult to compare the performances of the predicting models among institutions with different radiotherapy systems. Purpose: We aimed to develop unbiased scoring methods to evaluate the performance of the models predicting the GPR, by introducing both best and worst limits for the performance of the GPR prediction. Methods: Two hundred head‐and‐neck VMAT plans were used to develop a framework. The GPRs were measured using the ArcCHECK device. The predicted GPR [p] was generated using a deep learning‐based model [pDL]. The predicting model was evaluated using four metrics: standard deviation (SD) [σ], Pearson's correlation coefficient (CC) [r], mean squared error (MSE) [s], and mean absolute error (MAE) [a]. The best limit [σm${\sigma _m}$, rm${r_m}$, sm${s_m}$, and am${a_m}$] was estimated by measuring the SD of measured GPR [m] by shifting the device along the longitudinal direction to measure different sampling points. Mimicked best and worst p's [pbest and pworst] were generated from pDL. The worst limit was defined such that m and p have no correlation [CC ∼ 0]. The worst limit [σMix, rMix, sMix, and aMix] was generated using the event‐mixing (EM) technique originally introduced in high‐energy physics experiments. The range of σ, r, s, and a was defined to be [σm,σMix]$[ {{\sigma _m},{\sigma _{{\mathrm{Mix}}}}} ]$, [0,rm]$[ {0,{r_m}} ]$, [sm,sMix]$[ {{s_m},{s_{{\mathrm{Mix}}}}} ]$, and [am,aMix]$[ {{a_m},{a_{{\mathrm{Mix}}}}} ]$. The achievement score (AS) independently based on σ, r, s, and a were calculated for pDL, pbest and pworst. The probability that p fails the gamma analysis (alert frequency; AF) was estimated as a function of σd${\sigma _d}$ values within the [σm${\sigma _m}$, σMix] range for the 3%/2 mm data with a 95% criterion. Results: SDs of the best limit were well reproduced by σm=0.531100−m${\sigma _m} = \;0.531\sqrt {100 - m} $. The EM technique successfully generated the (m,p)$({m,p})$ pairs with no correlation. The AS using four metrics showed good agreement. This agreement indicates successful definitions of both best and worst limits, consistent definitions of the AS, and successful generations of mixed events. The AF for the DL‐based model with the 3%/2 mm tolerance was 31.5% and 63.0% with CL's 99% and 99.9%, respectively. Conclusion: We developed the AS to evaluate the predicting model of the GPR in an unbiased manner by excluding the effects of the precision of the radiotherapy system and the spreading of the GPR. The best and worst limits of the GPR prediction were successfully generated using the measured precision of the GPR and the EM technique, respectively. The AS and σp${\sigma _p}$ are expected to enable objective evaluation of the predicting model and setting exact achievement goal of precision for the predicted GPR. [ABSTRACT FROM AUTHOR]