Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues.

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.

Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.

BACKGROUND: In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. PURPOSE: To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. METHODS: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5-point scale (5, acceptable as-is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. RESULTS: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel-wise dose difference was -0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D 1 % ${D}_{1{\mathrm{\% }}}$ and D 98 % ${D}_{98{\mathrm{\% }}}$ were -1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were -0.30 ± 1.66 Gy and -0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. CONCLUSIONS: Deep-learning dose prediction can be used to predict high-quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.

A real-time contouring feedback tool for consensus-based contour training.

Purpose: Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. Methods: We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. Results: For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV. Conclusions: A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%,D95%, and D99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.

Historia de la Medicina Familiar en Colón / History of Family Medicine in Colón

RESUMEN La formación de un médico de nuevo modelo en Cuba surge de forma experimental por la necesidad que tenía la población de recibir una atención médica integral. Como siempre nuestro comandante con sus ideas revolucionarias plantea la necesidad de su creación para que cada familia cubana contara con un médico y una enfermera que les brindara apoyo y cuidado desde el punto de vista clínico, epidemiológico y social. El municipio de Colón fue el primero en implementar este novedoso programa en la provincia de Matanzas. Con el objetivo de dar a conocer el surgimiento y desarrollo del mismo en esta ciudad es que se realiza el siguiente trabajo (AU).

SUMMARY The training of a new model doctor in Cuba arises experimentally because of the need of the population to receive comprehensive medical care. As always, our commander with his revolutionary ideas raised the need for its creation so that each Cuban family would have a doctor and a nurse who could provide support and care from a clinical, epidemiological and social point of view. The municipality of Colón was the first to implement this novel program in the province of Matanzas. With the aim of publicizing its emergence and development in our city, the authors wrote the following article (AU).

Respuesta clínica a la combinación de acetilsalicilato de lisina y vitaminas B1, B6 y B12 / Treatment a mixture of lysine acetylsalicilate and vitamins B1, B6, B12

Se estudiaron 24 pacientes, 22 de los cuales sufrían algun tipo de neuritis. Se trataron con una mezcla de ACELTISALICILATO de LISINA y Vitaminas B1, B6, B12 incorporadas al solvente para diluir el analgésico. Diecisiete pacientes mejoraron entre un 80 y 100 por ciento de su dolor valorado con una escala gráfica de 1 a 10, o sea que el 70,83 por ciento de los pacientes obtuvieron una respuesta analgésica satisfactoria con minimos efectos secundarios que describieron los pacientes como un ligero dolor en el sitio de la inyecci¢n. A todos los pacientes se les administró una ampolla con la mezcla cada 24 horas, por vía intramuscular profunda, durante 4 días consecutivos

Estudio comparativo de las dosis de opioides recomendadas por la asociación internacional para el estudio del dolor (IASP) y de las dosis permitidas en Venezuela / Comparative study of opioids recomended dosages by the international association for the study of pain and the allowed dosages in Venezuela

Uno de los cuatro componentes del programa de control del cáncer de la Organización Mundial de la Salud (OMS) es el tratamiento del dolor asociado al cáncer y las restricciones gubernamentales con respecto aluso de opioides, constituye uno de los obstáculos de este programa. Con la idea de identificar las barreras en nuestro país se realizó este estudio para comparar las dosis recomendadas de opioides por la Asociación Internacional para el estudio del dolor (IASP) y las dosis permitidas por el Ministerio de Sanidad y Asistencia Social (MSAS) en Venezuela. Los datos fueron obtenidos de la Gaceta Oficial y del Task Force on Acute Pain de la IASP y se observó que todas las dosis permitidas de opioides por el MSAS en Venezuela se escuentran entre un 40 por ciento y 75 por ciento de las dosis recomendadas (IASP), con un menor porcentaje para la morfina, a pesar de ser este analgésico indicador de un buen tratamiento del dolor por cáncer. Sin embargo estas restricciones gubernamentales fueron modificadas y los médicos tratantes pueden aumentar las dosis si hacen un informe médico que lo justifique. La gaceta oficial no determina las dosis por vía oral ni parental de algunos opioides disponibles en el país

Acción analgésica de una nueva asociación analgésico-relajante muscular / Action analgescs of one new asociation muscle relaxants and analgesic

En un estudio simple ciego se estudió la eficiencia y tolerancia de un nuevo preparado a base de acetilsalicilato de lisina y pridinol mesilato en 56 pacientes que recibieron una dosis diaria del producto, por vía intramuscular, durante 5 días y que presentaban dolor y contractura muscular de diferentes localizaciones. Se obtuvieron buenos resultados en el 83.93 por ciento de los pacientes tratados, regulares en el 7,14 por ciento y solo hubo un 8,93 por ciento donde los resultados fueron nulos. En los pacientes que respondieron positivamente el comienzo de la acción analgésica fue rápido, pues ya desde la primera inyección se observó una franca mejoría del dolor. No se observaron efectos secundarios en ninguno de los pacientes tratados, salvo un ligero dolor moméntáneo en el sitio de la inyección en algunos pacientes