Manual Planning Research Articles

A novel automated planning approach for multi-anatomical sites cancer in Raystation treatment planning system

PurposeTo develop an automated planning approach in Raystation and evaluate its feasibility in multiple clinical application scenarios. MethodsAn automated planning approach (Ruiplan) was developed by using the scripting platform of Raystation. Radiotherapy plans were re-generated both automatically by using Ruiplan and manually. 60 patients, including 20 patients with nasopharyngeal carcinoma (NPC), 20 patients with esophageal carcinoma (ESCA), and 20 patients with rectal cancer (RECA) were retrospectively enrolled in this study. Dosimetric and planning efficiency parameters of the automated plans (APs) and manual plans (MPs) were statistically compared. ResultsFor target coverage, APs yielded superior dose homogeneity in NPC and RECA, while maintaining similar dose conformity for all studied anatomical sites. For OARs sparing, APs led to significant improvement in most OARs sparing. The average planning time required for APs was reduced by more than 43% compared with MPs. Despite the increased monitor units (MUs) for NPC and RECA in APs, the beam-on time of APs and MPs had no statistical difference. Both the MUs and beam-on time of APs were significantly lower than that of MPs in ESCA. ConclusionsThis study developed a new automated planning approach, Ruiplan, it is feasible for multi-treatment techniques and multi-anatomical sites cancer treatment planning. The dose distributions of targets and OARs in the APs were similar or better than those in the MPs, and the planning time of APs showed a sharp reduction compared with the MPs. Thus, Ruiplan provides a promising approach for realizing automated treatment planning in the future.

Comparison of Dose-Volume Histograms in Intracavitary Brachytherapy of Cervical Cancer using Manual Plan and Inverse Optimization Technique

Comparison of Dose-Volume Histograms in Intracavitary Brachytherapy of Cervical Cancer using Manual Plan and Inverse Optimization Technique

Autonomous grasping of 3-D objects by a vision-actuated robot arm using Brain–Computer Interface

A major drawback of a Brain–Computer Interface-based robotic manipulation is the complex trajectory planning of the robot arm to be carried out by the user for reaching and grasping an object. The present paper proposes an intelligent solution to the existing problem by incorporating a novel Convolutional Neural Network (CNN)-based grasp detection network that enables the robot to reach and grasp the desired object (including overlapping objects) autonomously using a RGB-D camera. This network uses a simultaneous object and grasp detection to affiliate each estimated grasp with its corresponding object. The subject uses motor imagery brain signals to control the pan and tilt angle of a RGB-D camera mounted on a robot link to bring the desired object inside its Field-of-view presented through a display screen while the objects appearing on the screen are selected using the P300 brain pattern. The robot uses inverse kinematics along with the RGB-D camera information to autonomously reach the selected object and the object is grasped using proposed grasping strategy. The overall BCI system outperforms other comparative systems involving manual trajectory planning significantly. The overall accuracy, steady-state error, and settling time of the proposed system are 93.4%, 0.05%, and 15.92 s, respectively. The system also shows a significant reduction of the workload of the operating subjects in comparison to manual trajectory planning based approaches for reaching and grasping.

Improving normal tissue sparing using scripting in endometrial cancer radiation therapy planning.

The aim of this study was to improve the protection of organs at risk (OARs), decrease the total planning time and maintain sufficient target doses using scripting endometrial cancer external beam radiation therapy (EBRT) planning. Computed tomography (CT) data of 14 endometrial cancer patients were included in this study. Manual and automatic planning with scripting were performed for each CT. Scripts were created in the RayStation™ (RaySearch Laboratories AB, Stockholm, Sweden) planning system using a Python code. In scripting, seven additional contours were automatically created to reduce the OAR doses. The scripted and manual plans were compared to each other in terms of planning time, dose-volume histogram (DVH) parameters, and total monitor unit (MU) values. While the mean total planning time for manual planning was 368 ± 8s, it was only 55 ± 2s for the automatic planning with scripting (p < 0.001). The mean doses of OARs decreased with automatic planning (p < 0.001). In addition, the maximum doses (D2% and D1%) for bilateral femoral heads and the rectum were significantly reduced. It was observed that the total MU value increased from 1146 ± 126 (manual planning) to 1369 ± 95 (scripted planning). It is concluded that scripted planning has significant time and dosimetric advantages over manual planning for endometrial cancer EBRT planning.

Technical note: First implementation of a one-stop solution of radiotherapy with full-workflow automation based on CT-linac combination.

Radiotherapy initiation is a laborious and time-consuming process that involves multiple steps and units. Workflow automation is in demand to improve the work efficiency and patient experience. The purposes of this study are to describe the technical characteristics and clinical performance of an AI-powered one-stop radiotherapy workflow for initial treatment based on CT-linac combination, and provide insight into the behavior of full-workflow automation in radiotherapy. Based on a CT-integrated linear accelerator and AI model implementation, the so-called "All-in-One" workflow incorporates routine procedures from simulation, autosegmentation, autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch. Clinical outcomes of the new workflow were evaluated for 10 enrolled patients with rectal cancer. For the enrolled patients, manual modifications of the autosegmented target volumes were necessary. The Dice similarity coefficient and 95% Hausdorff distance before and after the modifications were 0.892±0.061 and 18.2±13.0mm, respectively. The autosegmented normal tissues and automatic plans were clinically acceptable without any modifications or reoptimization. The pretreatment IGRT corrections were within 2mm in all directions, and the EPID-based in vivo QA showed γ passing rate of above 97% (3%/3mm/10% threshold) at all the checkpoints, better than the results of rectal patients who followed a routine workflow. The duration of the whole process was 23.2±3.5 minutes for the enrolled patients, depending mostly on the time required for manual modification and plan evaluation. The All-in-One workflow enables full-process automation of radiotherapy via seamless procedure integration. Compared to the routine workflow, the one-stop solution shortens the time scale it takes to ready the first treatment from days to minutes, significantly improving the patient experience and the workflow efficiency, and it also shows potential to facilitate clinical application of online adaptive replanning.

The effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT.

Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.

Implementation of automated personalised breast radiotherapy planning techniques with scripting in Raystation.

Implement scripted automatic breast planning (AP) for breast techniques within Raystation. Manual plans (MPs) were re-planned and compared with AP plans for whole breast (WB), partial breast (PB), hybrid volumetric modulated arc therapy simultaneous integrated boost (VMAT SIB) and VMAT nodal plans. WB AP plans took 7 min comparing well to MP. One WB AP failed a mandatory dose constraint. Small statistically significant differences showed improved coverage for AP at expense of slightly hotter plans, however absolute differences were small (mean differences < 1% or D 0.5cc<0.2 Gy). PB AP plans took 9 min, showing improved coverage (V 24.7Gy97.6 vs 96.4 %). One PB AP case failed a mandatory constraint. Other dosimetric differences were non-significant. SIB AP plans took 14 min with one case failing a mandatory constraint with minor differences compared with MP except larger V 42.8Gy (3 vs 1.5 %) and more MU. VMAT AP plans took 12 min and were hotter for PTVp_4000 but had higher nodal coverage. Contra_Lung V 2.5Gy was higher (8.8 %) than MP plans (6.5 %). Automatic planning of modern breast techniques has been successfully introduced using a commercial planning system. AP plans are very similar to MP, requiring little manual interaction for most cases with significant timesaving potential. Scripted breast plans produced within minutes for WB, PB, SIB and VMAT. Successfully introduced into large busy department. Plans similar to manual plans, requiring little manual interaction.

Multi-objective intelligent algorithm model design for housing environment optimization

With the improvement of the national living standard, the buyers have higher and higher requirements for the rationality and aesthetics of the spatial planning and layout of the residential area. The traditional residential space planning method is purely manual design, which is inefficient, and the design effect will be greatly affected by the designer’s work experience and personal aesthetics. Therefore, this research attempts to combine Pareto solution set and piecewise prediction idea into genetic algorithm, propose an algorithm for solving multi-objective optimization problems, and build an intelligent housing environment planning system based on this. The statistical results of simulation experiments show that the system can output more design schemes with better overall quality than the comparison system and manual planning results, and the stability of multiple operations is higher. When the number of iterations reaches 200, the average value of Pareto optimal solution number and optimal solution quality index QPS of the former is 44 and 0.41, respectively. The expert group analyzed the design results of this method and the manual method for an actual case, and found that the results designed by this method met the requirements and the calculation efficiency was much faster than manual processing. From the simulation test data and the actual case analysis, it can be seen that the intelligent housing environment planning system designed in this study is helpful to improve the efficiency of residential space design and the stability of residential space scheme style.

How To Prepare A Crisis Management Manual and Contingency Plans For The Construction Industry

How To Prepare A Crisis Management Manual and Contingency Plans For The Construction Industry

Design of a reinforcement learning-based intelligent car transfer planning system for parking lots.

In this study, a car transfer planning system for parking lots was designed based on reinforcement learning. The car transfer planning system for parking lots is an intelligent parking management system that is designed by using reinforcement learning techniques. The system features autonomous decision-making, intelligent path planning and efficient resource utilization. And the problem is solved by constructing a Markov decision process and using a dynamic planning-based reinforcement learning algorithm. The system has the advantage of looking to the future and using reinforcement learning to maximize its expected returns. And this is in contrast to manual transfer planning which relies on traditional thinking. In the context of this paper on parking lots, the states of the two locations form a finite set. The system ultimately seeks to find a strategy that is beneficial to the long-term development of the operation. It aims to prioritize strategies that have positive impacts in the future, rather than those that are focused solely on short-term benefits. To evaluate strategies, as its basis the system relies on the expected return of a state from now to the future. This approach allows for a more comprehensive assessment of the potential outcomes and ensures the selection of strategies that align with long-term goals. Experimental results show that the system has high performance and robustness in the area of car transfer planning for parking lots. By using reinforcement learning techniques, parking lot management systems can make autonomous decisions and plan optimal paths to achieve efficient resource utilization and reduce parking time.

Performance of auto-planning for VMAT hypofractionated left whole-breast irradiation with simultaneous integrated boost.

Retrospective analysis of volumetric modulated arc therapy plans for hypofractionated left whole-breast irradiation with simultaneous integrated boost to assess the performance of the auto-planning (AP) engine. Fifteen treatment plans, produced using manual optimization planning approach (MP) were replanned using (AP) approach. Dose-volume parameters were defined to quantify the quality of concurrent treatment plans assessing target coverage and sparing organs at risk (OARs). The Wilcoxon Signed Rank test was used for statistical comparison of all results obtained from the use of the 2 approaches. Dose coverage for both PTVs, PTVbreast, and PTVboost, were similar with AP showing slightly significantly better results for the homogeneity index for both PTVs, for D98% of PTVbreast and D2% of PTVboost. AP plans provided a significant reduction of dose for ipsilateral lung and contralateral lung. No significant differences were observed for heart and contralateral breast. A percentage difference of -14.0% was found for the mean dose of left coronary artery between AP plans and MP plans. Despite increase of total MU by 4.3% for AP plans, planning time resulted about half of that of the MP approach. Although PTVs doses were similar between MP and AP plans, AP plans generally spared OARs significantly better than MP plans. Furthermore, the shortest AP treatment plan time approach was attractive with respect to the workload.

Simulation-based optimization of product integrations in line-less assembly systems based on digital twins

Line-less assembly systems enable increased resilience against uncertain events such as frequent integration of new product variants, material unavailability, personnel shortages and station failures. This can be achieved through the resolution of a general takt time, redundant workstations and flexible precedence graphs. Benefits arise in particular for use cases with a high number of product variants, parallel assembly of various products, and frequent integration of new products and product variants. This work presents a service-based framework for decision-support for product integrations in line-less assembly systems to meet these challenges. Based on a standardized data model serving as a basis for the digital twin, simulation models for system configurations are created automatically to reduce manual planning effort.The approach of simulation-based optimization is used to find feasible scenarios with least reconfiguration effort. Within the simulation, stochastic influences such as station failures and reactive scheduling decisions can be considered. The developed framework is verified by implementing all modules and a specific optimization model for the automated generation of scenarios after integrating a new product into the line-less assembly system. It can be shown, that recommended actions for adding, removing, or reconfiguring workstations are generated to support a production planner.

MEMINV: A hybrid efficient approximation method solving the multi skill-resource constrained project scheduling problem.

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is an NP-Hard problem that involves scheduling activities while accounting for resource and technical constraints. This paper aims to present a novel hybrid algorithm called MEMINV, which combines the Memetic algorithm with the Inverse method to tackle the MS-RCPSP problem. The proposed algorithm utilizes the inverse method to identify local extremes and then relocates the population to explore new solution spaces for further evolution. The MEMINV algorithm is evaluated on the iMOPSE benchmark dataset, and the results demonstrate that it outperforms. The solution of the MS-RCPSP problem using the MEMINV algorithm is a schedule that can be used for intelligent production planning in various industrial production fields instead of manual planning.

A simulation-based factory layout planning approach using reinforcement learning

The planning process of a factory is of high importance for the future performance of the respective manufacturing system. However, the planning process is characterized by its high complexity, given the large variety of possible layout variants. Recent research has demonstrated that reinforcement learning can be successfully used in the early stage of factory layout planning to generate and optimize a factory layout. However, dynamic aspects in the material flow that influence the manufacturing system's throughput time are usually neglected since only analytic optimization objectives are considered. This paper addresses this research gap by presenting a reinforcement learning-based planning approach that incorporates a discrete event material flow simulation as part of the environment. The primary focus of investigation is the formulation of an appropriate reward function since the reward function has a direct influence on the solution quality. The results of the investigation show that the reinforcement learning approach is able to successfully interact with the simulation and optimize the layout with respect to the total throughput time if the incentives given by the reward function are aligned with the optimization objective. The results are additionally compared to a manual planning approach and the computed optimal solution which demonstrates that the developed approach is a suitable support tool for factory layout planning. Concluding, the approach is applied to an application scenario with 25 functional units to investigate the scalability of the approach.

Physician-modified endograft using three-dimensional model-assisted planning.

Case-specific and true-to-scale three-dimensional (3D) models have become increasingly useful tools for physician-modified endovascular grafting. This study aimed to validate the use of 3D model-assisted planning for fenestration design. Thirty-two consecutive patients (2019-2021) presenting with pararenal or juxtarenal abdominal aortic aneurysm (n= 16), paravisceral abdominal and Crawford's extent IV thoracoabdominal aortic aneurysm (n= 12), and type I endoleak after endovascular repair (n= 4) were analyzed retrospectively. All cases were planned manually with a standard method using curved planar reconstruction stretch images and multiplanar images perpendicular to the centerlines. The design was finalized by intraoperative 3D model-assisted planning. Intermethod agreements were assessed for geometrical relationships (separation heights and angles) between the superior mesenteric and renal arteries. The datasets from 55 double measurements of the entire cohort in this series were used to assess measurement discrepancies (≥3mm separation height or ≥15° angle difference) and fenestration mismatches (≥3mm separation between the manually planned and 3D model-assisted-planned renal arterial centers on the device surface) between manual and 3D model-assisted planning. Statistical analyses were performed to test the impact of anatomical factors on the discrepancies and mismatches. The imposition accuracy of 3D model-assisted planning and short-term clinical results of the 32 cases were also evaluated. Fourteen fenestration measurement discrepancies were detected. The size of the stent graft (P= .0381), the aortic angle (P= .0008), and the prior existence of stent graft (P= .0123) were found to have a statistically significant impact on the measurement discrepancy, using single logistic and Fisher's exact tests. Twelve fenestration mismatches were observed and found to be significantly affected (P= .0039) by aortic angle. A cutoff value for fenestration mismatch was found to be 36.5°, with a sensitivity and specificity of 69.2% and 80.5%, respectively, using receiver operating characteristic analysis (area under the curve, 0.782± 0.081; P= .0023). A high level of branch preservation (100%) was achieved. During the observation period (1.3years on average; range, 0.5-2.5years), no patient experienced complications related to fenestration. The differences between the planning methods were non-negligible. However, 3D model-assisted planning increased the precision of the fenestration design when the conformation of the stent graft to the aortic anatomy is taken into account.

Survey on the Current Status of Orthopedic Manual Therapy

With the emergence of an aging society, the number of orthopedic manual therapy prescriptions is increasing. However, no clear prescription guidelines exist for orthopedic manual therapy in the Republic of Korea. This study aimed to examine the current status of orthopedic manual therapy in the Republic of Korea by examining the satisfaction, time, and cost adequacy of orthopedic manual therapy for patients. This survey was conducted among adults who visited a clinic or hospital in the Republic of Korea. The survey had 26 questions and was distributed through a Google questionnaire URL. This study performed descriptive statistics and Chi-squared test of the general characteristics of the respondents, orthopedic manual therapy experience, treated hospital type, perception, expectation, satisfaction, and the right price and duration for orthopedic manual therapy. Of the total 278 respondents, 113 (40.6%) had undergone orthopedic manual therapy previously, and 165 had not experienced the therapy (59.4%). Orthopedic manual therapy was mainly performed twice a week for 30-60 min, and the price was between 100,000 and 140,000 won per session. The desired price and time were 50,000-90,000 won and 30-60 min, respectively. The longer the duration of receiving orthopedic manual therapy and the greater the time/frequency satisfaction, and the effect of the therapy. Activities of daily living (ADL) are particularly likely to improve when receiving orthopedic manual therapy at a rehabilitation hospital than others. Satisfaction with orthopedic manual therapy increases as time/frequency satisfaction increases and ADL improves. Thus, it is important to suggest an appropriate manual therapy time according to patients’ circumstances and develop a manual therapy plan to improve ADL.

Toward real-time automatic treatment planning (RTTP) with a one-step 3D fluence map prediction method and (nonorthogonal) convolution technique

PurposeTo establish and evaluate a (quasi) real-time automated treatment planning (RTTP) strategy utilizing a one-step full 3D fluence map prediction model based on a nonorthogonal convolution operation for rectal cancer radiotherapy. MethodsThe RTTP approach directly extracts 3D projections from volumetric CT and anatomical data according to the beam incident direction. A 3D deep learning model with a nonorthogonal convolution operation was established that takes projections in cone beam space as input, extracts the features along and around the ray-trace path, and outputs a predicted fluence map (PFM) for each beam. The PFM is then converted to the MLC sequence with deliverable MUs to generate the final treatment plan. A total of 314 rectal adenocarcinoma patients with 2198 projection data samples were used in model training and validation. An extra 20 patients were used to test the feasibility of the RTTP method by comparing the plan quality, efficiency, deliverability performance, and physician blinded review results with the manual plans. ResultsOverall, the RTTP plans met the clinical dose criteria for target coverage, conformity, homogeneity, and organ-at-risk dose sparing. Compared to manual plans, the RTTP plans showed increases in PTV D1% by only 2.33% (p < 0.001) and a decrease in PTV D99% by 0.45% (p < 0.05). The RTTP plans showed a dose increase in the bladder, with a V50 of 14.01 ± 11.75% vs. 10.74 ± 8.51%, respectively, and no significant increases in the femoral head with the mean dose. The planning efficiency was improved in RTTP planning, with 39 s vs. 944 s in fluence map generation; the deliverability performance was saved by 1.91% (p < 0.001) in total MU. According to the blinded plan review by our physician, 55% of RTTP plans can be directly used in clinical radiotherapy treatment. ConclusionThe quasi RTTP method improves the planning efficiency and deliverability performance while maintaining a plan quality close to that of the optimized manual plans in rectal radiotherapy.

Comprehensive dosimetric and clinical evaluation of lexicographic optimization-based planning for cervical cancer.

In this study, a not yet commercially available fully-automated lexicographic optimization (LO) planning algorithm, called mCycle (Elekta AB, Stockholm, Sweden), was validated for cervical cancer. Twenty-four mono-institutional consecutive treatment plans (50 Gy/25 fx) delivered between November 2019 and April 2022 were retrospectively selected. The automatic re-planning was performed by mCycle, implemented in the Monaco TPS research version (v5.59.13), in which the LO and Multicriterial Optimization (MCO) are coupled with Monte Carlo calculation. mCycle optimization follows an a priori assigned priority list, the so-called Wish List (WL), representing a dialogue between the radiation oncologist and the planner, setting hard constraints and following objectives. The WL was tuned on a patient subset according to the institution's clinical protocol to obtain an optimal plan in a single optimization. This robust WL was then used to automatically re-plan the remaining patients. Manual plans (MP) and mCycle plans (mCP) were compared in terms of dose distributions, complexity (modulation complexity score, MCS), and delivery accuracy (perpendicular diode matrices, gamma analysis-passing ratio, PR). Their clinical acceptability was assessed through the blind choice of two radiation oncologists. Finally, a global quality score index (SI) was defined to gather into a single number the plan evaluation process. The WL tuning requested four patients. The 20 automated re-planning tasks took three working days. The median optimization and calculation time can be estimated at 4h and just over 1h per MP and mCP, respectively. The dose comparison showed a comparable organ-at-risk spare. The planning target volume coverage increased (V95%: MP 98.0% [95.6-99.3]; mCP 99.2%[89.7-99.9], p >0.05). A significant increase has been registered in MCS (MP 0.29 [0.24-0.34]; mCP 0.26 [0.23-0.30], p <0.05) without affecting delivery accuracy (PR (3%/3mm): MP 97.0% [92.7-99.2]; mCP 97.1% [95.0-98.6], p>0.05). In the blind choice, all mCP results were clinically acceptable and chosen over MP in more than 75% of cases. The median SI score was 0.69 [0.41-0.84] and 0.73 [0.51-0.82] for MP and mCP, respectively (p >0.05). mCycle plans were comparable to clinical manual plans, more complex but accurately deliverable and registering a similar SI. Automated plans outperformed manual plans in blinded clinical choice.

A metaheuristic algorithm for a locomotive routing problem arising in the steel industry

In this study, we address the locomotive routing problem faced in the steel industry. During steel production, locomotives are employed to move torpedo cars, which transport molten iron, between blast furnaces and converters. The resulting complex pickup and delivery routing system which features multiple types of practical constraints, such as hard time windows, incompatibility of requests, variable locomotives capacity, and last-in-first-out constraints, constitutes this problem. Herein, three-index and modified group-based formulations are described and an adaptive large neighborhood search (ALNS) algorithm is proposed as a solution. The ALNS algorithm relies on both adapted and novel removal and insertion operators and a feature-based local search procedure. The mathematical formulations and the ALNS algorithm are evaluated on instances generated according to the actual production process. The results validate the effectiveness of the algorithm. A comparison with the manual plan also verifies the quality of the solutions produced by the algorithm.

Dosimetric Influence of Normalization Points on Post Mastectomy Chest Wall Teletherapy

Background: Many advanced radiotherapy techniques had been employed in breast cancer teletherapy, purposely to significantly reduce dose to organs at risk (heart and lungs) with marginal or no compromise in planning target volume (PTV). Majority of used techniques yielded rewarding results in developed countries where facilities, manpower and skills are available. In Nigeria, significant number of post mastectomy breast cancer patients received chest wall irradiation using manual hand planning, therefore, the dose distributions to target volume and organs at risk (OAR) were uncertain. Sokoto centre being the first in the country to use treatment planning system (TPS) focused on identifying planning skills (normalization points) with good 95% dose coverage to PTV, and minimizing dose to OAR. Methods: Eighteen post mastectomy patients (ten rights and eight lefts chest walls) were simulated via computed tomography scan (CT-scan) in supine position with breast board and fudicial markers to demarcate tumour bed borders. Planning target volume (PTV chest walls) and OAR were contoured from the acquired CT images and bi-tangential portals were applied. The energy used from Elekta précised Linac was 6 MV, and dose of 50Gy in 25# was prescribed to each patient. The Upper 1/3rd normalization point (UNP), Lower 1/3rd (LNP) and Inter-field (INP) were sequentially applied as dose normalization points on each planning CT image, dose to PTV and OAR were evaluated using Clarkson and pencil beam calculation algorithms. Statistical analysis was conducted using SPSS Software to study dose distributions of the normalization points. Results: Patients simulated were between the ages of 29-56yrs with mean age of 42yrs. The mean percentage doses from normalization points on left chest walls ranged between 81.7-107.7% to PTV, 13.3-17.8% to the lung and 5.5-6.8% to the heart, the reported hot spots were between 110.7 to 141.5%. Similarly, the mean doses from normalization points on the right chest walls ranged between 81.5-108.8% for PTV, 17.8-23.5% to the lung and 2.7-3.7% to the heart, with hot spots of 108.8-137.9%. The statistical differences using independent-t- test for the normalization points on both left and right chest walls shows p-value &lt; 5%. Conclusion: The three normalization points influenced dose distribution to PTV and OAR differently. The UNP and LNP showed a desired dosimetry with marginal compromise in 95% PTV coverage compared to INP.