Utilizing collimated aperture with proton pencil beam scanning (PBS) for stereotactic radiotherapy.

PURPOSE: Proton stereotactic radiosurgery (PSRS) has emerged as an innovative proton therapy modality aimed at achieving precise dose delivery with minimal impact on healthy tissues. This study explores the dosimetric outcomes of PSRS in comparison to traditional intensity-modulated proton therapy (IMPT) by focusing on cases with small target volumes. A custom-made aperture system designed for proton therapy, specifically tailored to small target volumes, was developed and implemented for this investigation. METHODS: A prerequisite mechanical validation through an isocentricity test precedes dosimetric assessments, ensuring the seamless integration of mechanical and dosimetry analyses. Five patients were enrolled in the study, including two with choroid melanoma and three with arteriovenous malformations (AVM). Two treatment plans were meticulously executed for each patient, one utilizing a collimated aperture and the other without. Both plans were subjected to robust optimization, maintaining identical beam arrangements and consistent optimization parameters to account for setup errors of 2 mm and range uncertainties of 3.5%. Plan evaluation metrics encompassing the Heterogeneity Index (HI), Paddick Conformity Index (CIPaddick), Gradient Index (GI), and the R50% index to evaluate alterations in low-dose volume distribution. RESULTS: The comparative analysis between PSRS and traditional PBS treatment revealed no significant differences in plan outcomes, with both modalities demonstrating comparable target coverage. However, collimated apertures resulted in discernible improvements in dose conformity, dose fall-off, and reduced low-dose volume. CONCLUSIONS: This study underscores the advantageous impact of the aperture system on proton therapy, particularly in cases involving small target volumes.

Clinical and Dosimetric Results of Proton or Photon Radiation Therapy for Large (>5 cm) Hepatocellular Carcinoma: A Retrospective Analysis.

PURPOSE: Our purpose was to report the clinical and dosimetric attributes of patients with large unresectable hepatocellular carcinoma (HCC) undergoing proton or photon radiation therapy. METHODS AND MATERIALS: We retrospectively analyzed the outcomes and dosimetric indices of 159 patients with >5 cm nonmetastatic HCC who underwent definitive radiation therapy using either protons (N = 105) or photons (N = 54) between 2014 and 2018. Additional photon plans were performed in the 105 proton-treated patients using the same dose prescription criteria for intragroup dosimetric comparison. RESULTS: After a median follow-up of 47 months, patients with biologically effective dose (BED10) ≥ 75 Gy exhibited significantly better local control (LC; 2-year: 85.6% vs 20.5%; P < .001), progression-free survival (PFS; median, 7.4 vs 3.2 months; P < .001), and overall survival (OS; median, 18.1 vs 7.3 months; P < .001) compared with those with BED10 < 75 Gy. Notably, proton-treated patients had a significantly higher BED10 (96 vs 67 Gy; P < .001) and improved LC (2-year: 88.5% vs 33.8%; P < .001), PFS (median, 7.4 vs 3.3 months; P = .001), and OS (median, 18.9 vs 8.3 months; P < .001) than those undergoing photon radiation therapy. Furthermore, patients treated with protons had significantly lower V1 of the liver (P < .001), mean upper gastrointestinal tract dose (P < .001), and mean splenic dose (P < .001), with significantly decreased incidences of radiation-induced liver disease (P = .007), grade ≥3 upper gastrointestinal bleeding (P = .001), and grade ≥3 lymphopenia (P = .003). On multivariate analysis, proton radiation therapy consistently correlated with superior LC (P < .001), PFS (P < .001), and OS (P < .001). In intragroup dosimetric comparison, photon plans demonstrated significantly higher mean liver dose (P < .001) compared with actually delivered proton treatments, and 72 (69%) of them had mean liver dose exceeding 28 Gy, which necessitated target dose de-escalation. CONCLUSIONS: In the context of large HCC radiation therapy, a higher target BED10 was associated with improved outcomes. Notably, proton therapy has demonstrated the capability to deliver ablative doses while also being accompanied by fewer instances of severe toxicity.

Development and validation of a machine learning model of radiation-induced hypothyroidism with clinical and dose-volume features.

BACKGROUND AND PURPOSE: Radiation-induced hypothyroidism (RIHT) is a common but underestimated late effect in head and neck cancers. However, no consensus exists regarding risk prediction or dose constraints in RIHT. We aimed to develop a machine learning model for the accurate risk prediction of RIHT based on clinical and dose-volume features and to evaluate its performance internally and externally. MATERIALS AND METHODS: We retrospectively searched two institutions for patients aged >20 years treated with definitive radiotherapy for nasopharyngeal or oropharyngeal cancer, and extracted their clinical information and dose-volume features. One was designated the developmental cohort, the other as the external validation cohort. We compared the performances of machine learning models with those of published normal tissue complication probability (NTCP) models. RESULTS: The developmental and external validation cohorts consisted of 378 and 49 patients, respectively. The estimated cumulative incidence rates of grade ≥1 hypothyroidism were 53.5% and 61.3% in the developmental and external validation cohorts, respectively. Machine learning models outperformed traditional NTCP models by having lower Brier scores at every time point and a lower integrated Brier score, while demonstrating a comparable calibration index and mean area under the curve. Even simplified machine learning models using only thyroid features performed better than did traditional NTCP algorithms. The machine learning models showed consistent performance between folds. The performance in a previously unseen external validation cohort was comparable to that of the cross-validation. CONCLUSIONS: Our model outperformed traditional NTCP models, with additional capabilities of predicting the RIHT risk at individual time points. A simplified model using only thyroid dose-volume features still outperforms traditional NTCP models and can be incorporated into future treatment planning systems for biological optimization.

Multi-Institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume Using Planning CT and FDG-PET/CT.

BACKGROUND: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation with high labor costs and inter-user variability. PURPOSE: To validate the clinical applicability of a deep learning multimodality esophageal GTV contouring model, developed at one institution whereas tested at multiple institutions. MATERIALS AND METHODS: We collected 606 patients with esophageal cancer retrospectively from four institutions. Among them, 252 patients from institution 1 contained both a treatment planning CT (pCT) and a pair of diagnostic FDG-PET/CT; 354 patients from three other institutions had only pCT scans under different staging protocols or lacking PET scanners. A two-streamed deep learning model for GTV segmentation was developed using pCT and PET/CT scans of a subset (148 patients) from institution 1. This built model had the flexibility of segmenting GTVs via only pCT or pCT+PET/CT combined when available. For independent evaluation, the remaining 104 patients from institution 1 behaved as an unseen internal testing, and 354 patients from the other three institutions were used for external testing. Degrees of manual revision were further evaluated by human experts to assess the contour-editing effort. Furthermore, the deep model's performance was compared against four radiation oncologists in a multi-user study using 20 randomly chosen external patients. Contouring accuracy and time were recorded for the pre- and post-deep learning-assisted delineation process.

Synthesis of hexagonal ZnO clubs with opposite faces of unequal dimensions for the photoanode of dye-sensitized solar cells.

Novel sub-micro sized hexagonal clubs of ZnO (HC-ZnO), which are coated as a scattering layer (SL) for the photoanode of a DSSC, are synthesized. X-ray diffraction (XRD) patterns of the ZnO clubs show clear peaks corresponding to wurtzite crystal phase of ZnO. Scanning electron microscopic (SEM) images show that each club has two opposite hexagonal faces (parts) of unequal dimensions. High resolution transmission electron microscopic (HR-TEM) image of a single ZnO club reveals that the ZnO is single crystalline and has wurtzite crystal structure; the image indicates a lattice spacing (d) of 0.26 nm; this is ascribed to the (002) planar spacing of the hexagonal ZnO. A solar-to-electricity conversion efficiency (η) of 3.36% is achieved for the cell with the double layer (DL) film, which is 16% higher than that of the cell with only transparent layer (TL) of commercial ZnO (2.89%) and far higher than that of the cell with SL (0.05%). The η of the cell with the DL (3.36%) could further be improved to 4.28% through the modification of the DL surface with TiO(x). Incident photo-to-current conversion efficiency (IPCE) curves, UV-vis absorption spectra, energy dispersive X-ray (EDX) spectra, and electrochemical impedance spectra (EIS) are also used to substantiate the results.