Sep 2026· Medical Physics· 1 citation· 23 references
Advanced Radiotherapy Techniques
Abstract
Abstract Background Consistently generating clinically acceptable plans without human intervention remains a challenge in radiotherapy. Rule‐based automation provides deterministic execution, and knowledge‐based planning (KBP) provides statistical dose estimation, but both often require manual refinement. Large language models (LLMs) offer clinical reasoning capability, but effective autonomous planning also requires a mechanism to execute complex planning actions within the treatment planning system (TPS). Purpose To develop and evaluate PlanningCopilot, an agentic system that utilizes the reasoning capability of LLM and a validated Eclipse Scripting API (ESAPI) optimization module integrating KBP initialization (“PlanAct”) to autonomously generate treatment plans. This study evaluates the system's ability to produce clinically acceptable plans for locally advanced non‐small cell lung cancer (LA‐NSCLC) and assesses its potential to refine performance by self‐learning. Methods PlanningCopilot was implemented as a multi‐agent framework linked to the TPS through PlanAct API. It comprises four specialized GPT‐4.1 agents that iteratively interact with the TPS: (1) an Evaluator agent that accesses the plan and generates plan quality reports, (2) a Supervisor agent that validates these reports before passing them to a Planner agent, (3) the Planner agent that executes initialization and optimization tasks through PlanAct API and planning guidelines, and (4) an optional Learner agent that synthesizes optimization history into Planner‐facing prompt addendums. We retrospectively analyzed 62 patients with conventionally fractionated LA‐NSCLC and compared original clinical plans with autonomous plans with and without the Learner agent. Measurement‐based patient‐specific quality assurance (PSQA) was performed on the first 21 autonomous IMRT plans in planning order. Results All autonomous plans met clinical dosimetric requirements, including those not achieved in the clinical plans and KBP (RapidPlan) plans. Paired Wilcoxon signed‐rank tests showed no significant differences between autonomous and clinical plans for Lungs D mean ( p = 0.371), Lungs V 20Gy ( p = 0.449), Lungs V 5Gy ( p = 0.309), Heart D 50% ( p = 0.175), Esophagus D mean ( p = 0.750), Spinal Cord D 0.03cc ( p = 0.422), and Plan D 0.03cc ( p = 0.941). Furthermore, autonomous plans achieved significantly lower Esophagus D 0.03cc ( p = 0.027). Compared with RapidPlan initialization, PlanningCopilot improved multiple dosimetric endpoints, including Lungs D mean ( p < 0.001), Lungs V 20Gy ( p < 0.001), Lungs V 5Gy ( p = 0.004), Heart D 50% ( p = 0.037), and Plan D 0.03cc ( p < 0.001), with the cost of higher Esophagus D mean ( p < 0.001) and Esophagus D 0.03cc ( p < 0.001). In a subset of 18 cases requiring at least two iterations, applying Learner‐derived knowledge reduced required iterations by an average of 11.8% while maintaining comparable plan quality ( p > 0.05). All 21 autonomous IMRT plans passed measurement‐based PSQA. Conclusion PlanningCopilot enables autonomous generation of clinically acceptable and deliverable treatment plans for LA‐NSCLC. It consistently satisfies clinical dosimetric requirements across varying anatomical complexities and improves optimization efficiency through self‐learning from prior optimization history.
Software startups are newly created companies with no operating history and oriented towards producing cutting-edge products. However, despite the increasing importance of startups in the economy, few scientific studies attempt to address software engineering issues, especially for early-stage startups. If anything, startups need engineering practices of the same level or better than those of larger companies, as their time and resources are more scarce, and one failed project can put them out of business. In this study we aim to improve understanding of the software development strategies employed by startups. We performed this state-of-practice investigation using a grounded theory approach. We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible. This strategy allows startups to verify product and market fit, and to adjust the product trajectory according to early collected user feedback. The need to shorten time-to-market, by speeding up the development through low-precision engineering activities, is counterbalanced by the need to restructure the product before targeting further growth. The resulting implications of the GSM outline challenges and gaps, pointing out opportunities for future research to develop and validate engineering practices in the startup context.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 179 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models. Software startup ...
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
In the context of software startups, project failure is embraced actively and considered crucial to obtain validated learning that can lead to pivots. A pivot is the strategic change of a business concept, product or the different elements of a business model. A better understanding is needed on different types of pivots and different factors that lead to failures and trigger pivots, for software entrepreneurial teams to make better decisions under chaotic and unpredictable environment. Due to the nascent nature of the topic, the existing research and knowledge on the pivots of software startups are very limited. In this study, we aimed at identifying the major types of pivots that software startups make during their startup processes, and highlighting the factors that fail software projects and trigger pivots. To achieve this, we conducted a case survey study based on the secondary data of the major pivots happened in 49 software startups. 10 pivot types and 14 triggering factors were identified. The findings show that customer need pivot is the most common among all pivot types. Together with customer segment pivot, they are common market related pivots. The major product related pivots are zoom-in and technology pivots. Several new pivot types were identified, including market zoom-in, complete and side project pivots. Our study also demonstrates that negative customer reaction and flawed business model are the most common factors that trigger pivots in software startups. Our study extends the research knowledge on software startup pivot types and pivot triggering factors. Meanwhile it provides practical knowledge to software startups, which they can utilize to guide their effective decisions on pivoting.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
To compete in this age of disruption, large companies cannot rely on cost efficiency, lead time reduction and quality improvement. They are now looking for ways to innovate like startups. Meanwhile, the awareness and use of the Lean startup approach have grown rapidly amongst the software startup community in recent years. This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors. A multiple case study approach is followed in the investigation. Two software product innovation projects from two large companies are examined, using a conceptual framework that is based on the method-in-action framework and extended with the previously developed Lean-Internal Corporate Venture model. Seven face-to-face in-depth interviews of the employees with different roles are conducted. Within-case analysis and cross-case comparison are applied to draw the findings from the cases. A generic process flow summarises the common key processes of Lean internal startups. The findings suggest that an internal startup that is initiated management or employees faces different challenges. A list of enablers of applying Lean startup in large companies are identified, including top management support and cross-functional team. Both cases face different inhibitors due to the different process of inception, objective of the team and type of the product. Our contributions are threefold. First, this study is one of the first attempt to investigate the use of Lean startup approach in large companies empirically. Second, the study shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context. The third is a general process of Lean internal startup and the evidence of the enablers and inhibitors of implementing it, which are both theory-informed and empirically grounded.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9