Efficiency of KTP laser for verrucae vulgaris and plantaris: A retrospective analysis.

BACKGROUND: Verrucae are benign proliferations caused by human papillomavirus. Treatment is needed for pain, bleeding, contagiousness, and cosmetically. AIMS: The aim of this study is to evaluate the effectiveness of KTP laser treatment in verrucae, as well as side effects and recurrence rates. PATIENTS AND METHODS: Patients who had Verrucae vulgaris (V. vulgaris) and Verrucae plantaris (V. plantaris) previously treated with KTP laser were scanned for their eligibility. Twenty-nine patients who met the inclusion criteria were included. There was complete recovery in 24 patients (82.8%) and partial recovery in two patients (6.9%). Three patients (10.3%) were unresponsive to treatment after six sessions (no recovery). Two patients had mild pain during the application, and 65.4% of the patients had complete cosmetic recovery at the 3rd month follow-up. RESULTS: As a result, we found that the standard KTP laser can be an effective option for both types of verrucae. CONCLUSIONS: We think KTP laser treatment can be preferred for verrucae treatment.

Automated VMAT treatment planning using sequential convex programming: algorithm development and clinical implementation.

Objective.To develop and clinically implement a fully automated treatment planning system (TPS) for volumetric modulated arc therapy (VMAT).Approach.We solve two constrained optimization problems sequentially. The tumor coverage is maximized at the first step while respecting all maximum/mean dose clinical criteria. The second step further reduces the dose at the surrounding organs-at-risk as much as possible. Our algorithm optimizes the machine parameters (leaf positions and monitor units) directly and the resulting mathematical non-convexity is handled using thesequential convex programmingby solving a series of convex approximation problems. We directly integrate two novel convex surrogate metrics to improve plan delivery efficiency and reduce plan complexity by promoting aperture shape regularity and neighboring aperture similarity. The entire workflow is automated using the Eclipse TPS application program interface scripting and provided to users as a plug-in, requiring the users to solely provide the contours and their preferred arcs. Our program provides the optimal machine parameters and does not utilize the Eclipse optimization engine, however, it utilizes the Eclipse final dose calculation engine. We have tested our program on 60 patients of different disease sites and prescriptions for stereotactic body radiotherapy (paraspinal (24 Gy × 1, 9 Gy × 3), oligometastis (9 Gy × 3), lung (18 Gy × 3, 12 Gy × 4)) and retrospectively compared the automated plans with the manual plans used for treatment. The program is currently deployed in our clinic and being used in our daily clinical routine to treat patients.Main results.The automated plans found dosimetrically comparable or superior to the manual plans. For paraspinal (24 Gy × 1), the automated plans especially improved tumor coverage (the average PTV (Planning Target Volume) 95% from 96% to 98% and CTV100% from 95% to 97%) and homogeneity (the average PTV maximum dose from 120% to 116%). For other sites/prescriptions, the automated plans especially improved the duty cycle (23%-39.4%).Significance.This work proposes a fully automated approach to the mathematically challenging VMAT problem. It also shows how the capabilities of the existing (Food and Drug Administration)FDA-approved commercial TPS can be enhanced using an in-house developed optimization algorithm that completely replaces the TPS optimization engine. The code and pertained models along with a sample dataset will be released on our ECHO-VMAT GitHub (https://github.com/PortPy-Project/ECHO-VMAT).

Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy.

Each year, approximately 18 million new cancer cases are diagnosed worldwide, and about half must be treated with radiotherapy. A successful treatment requires treatment planning with the customization of penetrating radiation beams to sterilize cancerous cells without harming nearby normal organs and tissues. This process currently involves extensive manual tuning of parameters by an expert planner, making it a time-consuming and labor-intensive process, with quality and immediacy of critical care dependent on the planner's expertise. To improve the speed, quality, and availability of this highly specialized care, Memorial Sloan Kettering Cancer Center developed and applied advanced optimization tools to this problem (e.g., using hierarchical constrained optimization, convex approximations, and Lagrangian methods). This resulted in both a greatly improved radiotherapy treatment planning process and the generation of reliable and consistent high-quality plans that reflect clinical priorities. These improved techniques have been the foundation of high-quality treatments and have positively impacted over 4,000 patients to date, including numerous patients in severe pain and in urgent need of treatment who might have otherwise required longer hospital stays or undergone unnecessary surgery to control the progression of their disease. We expect that the wide distribution of the system we developed will ultimately impact patient care more broadly, including in resource-constrained countries.

Solving the volumetric modulated arc therapy (VMAT) problem using a sequential convex programming method.

The volumetric modulated arc therapy (VMAT) problem is highly non-convex and much more difficult than the fixed-field intensity modulated radiotherapy optimization problem. To solve it efficiently, we propose a sequential convex programming algorithm that solves a sequence of convex optimization problems. Beginning by optimizing the aperture weights of many (72) evenly distributed beams using the beam's eye view of the target from each direction as the initial aperture shape, the search space is constrained to allowing the leaves to move within a pre-defined step-size. A convex approximation problem is introduced and solved to optimize the leaf positions and the aperture weights within the search space. The algorithm is equipped with both local and global search strategies, whereby a global search is followed by a local search: a large step-size results in a global search with a less accurate convex approximation, followed by a small step-size local search with an accurate convex approximation. The performance of the proposed algorithm is tested on three patients with three different disease sites (paraspinal, prostate and oligometastasis). The algorithm generates VMAT plans comparable to the ideal 72-beam fluence map optimized plans (i.e. IMRT plans before leaf sequencing) in 14 iterations and 36 mins on average. The algorithm is also tested on a small down-sampled prostate case for which we could computationally afford to obtain the ground-truth by solving the non-convex mixed-integer optimization problem exactly. This general algorithm is able to produce results essentially equivalent to the ground-truth but 12 times faster. The algorithm is also scalable and can handle real clinical cases, whereas the ground-truth solution using mixed-integer optimization can only be obtained for highly down-sampled cases.

A column generation heuristic for VMAT planning with adaptive CVaR constraints.

In this study we develop an efficient computational procedure that generates medically acceptable treatment plans for volumetric modulated arc therapy with constant gantry speed. Our proposed method is a column generation heuristic based on a mixed integer linear programming model, where the objective function contains minimization of total monitor unit of the treatment plan and dose-volume requirements are included as conditional value-at-risk constraints. Our heuristic generates a full treatment arc for the restricted master problem and calibrates the right hand side parameters of the conditional value-at-risk constraints in the first phase. In the second phase, this initial solution is improved by performing column generation. This is a fully automated procedure and produces treatment plans in a single call without any human intervention. We evaluate its performance on real prostate cancer data by comparing the quality of the generated plans with those obtained by a widely used commercial treatment planning system. Our analysis shows that the results are promising, and the generated plans satisfy the prescription restrictions and require [Formula: see text] fewer monitor units on average compared to the ones obtained using Eclipse.

The Relationship between ABO Blood Groups and Acne Vulgaris.

BACKGROUND AND AIM: Studies of associations between various cancers and the ABO blood groups have shown elevated relative risks for some categories of disease. There has so far been no report of an evaluation of the relationship between the ABO blood groups and acne vulgaris. To investigate this association, we conducted a retrospective study of acne vulgaris diagnosed in Turkey. MATERIAL AND METHODS: All cases were clinically confirmed. Blood information was obtained on 498 individuals with acne vulgaris, and the distribution of ABO and Rh blood type for cases was compared with that of 419 healthy blood donors from the same geographic area. RESULTS: Patients with group A and B blood groups ratios were higher than the control group, but not statistically significant (P = 0.325 and P = 0.138). The ratio of the patient group with AB blood group was significantly higher than in the control group (P < 0.01). The ratio of blood group O of patient group was significantly lower than in the control group (P < 0.01). There were no statistically significant differences between the patient and control groups in the distribution of Rh factor. CONCLUSION: Our study showed a significant association of AB and O blood groups with acne vulgaris. Further studies in a larger series on blood group antigens are needed to shed some light on the relationship between these antigens and skin cancer.