Objective To measure the CT Hounsfield Unit ( HU) and relative stopping power ( RSP) conversion curve. Methods In this study, the RSPs of 12 different tissue equivalent rods were measured with proton and carbon beam in the Shanghai Proton and Heavy Ion Center ( SPHIC) . The same tissue equivalent materials were scanned with CT scanner to acquire the HU. Results Conversion curve for the transformation of HU into RSP was generated for both proton and carbon ion beam. Differences between RSPs measured using proton and carbon beam were ≤0. 64%except lung material. Conclusions A RSP versus HU conversion curve was generated for both protons and carbon ions.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Objective: To verify the safety and efficacy of IONTRIS particle therapy system (IONTRIS) in clinical implementation. Methods: Between 6.2014 and 8.2014, a total of 35 patients were enrolled into this trial: 31 males and 4 females with a median age of 69 yrs (range 39-80). Ten patients had locally recurrent head and neck tumors after surgery, 4 cases with thoracic malignancies, 1 case with hepatocellular carcinoma, 1 case with retroperitoneal sarcoma, and 19 cases with non-metastatic prostate carcinomas. Phantom dose verification was mandatory for each field before the start of radiation. Results: Twenty-two patients received carbon ion and 13 had proton irradiation. With a median follow-up time of 1 year, all patients were alive. Among the 16 patients with head and neck, thoracic, and abdominal/pelvic tumors, 2, 1, 12, and 1 cases developed complete response, partial response, stable disease, or disease progression, respectively. Progression-free survival rate was 93.8% (15/16). Among the 19 patients with prostate cancer, biological-recurrence free survival was 100%. Particle therapy was well tolerated in all 35 patients. Twenty-five patients (71.4%) experienced 33 grade 1 acute adverse effects, which subsided at 1 year follow-up. Six (17.1%) patients developed grade 1 late adverse effects. No significant change in ECOG or body weight was observed. Conclusions IONTRIS is safe and effective for clinical use. However, long term follow-up is needed to observe the late toxicity and long term result.