Forty-three consecutive patients with metastatic breast cancer and clearly measurable disease were treated with sequential multiagent chemotherapy. Therapy consisted of the administration in fixed sequence of cisplatin, doxorubicin, and cyclophosphamide (PAC) (four cycles), vinblastine, doxorubicin, and dexamethasone (VAD) (six cycles), and VP-16, methotrexate, and 5-fluorouracil (VMF) (six cycles). At the conclusion of 16 cycles of chemotherapy, all treatment was stopped. Patients were assessed for toxicity and disease response after each treatment. Duration of response and survival rate were determined for 41 evaluable patients. The overall response rate was 80% with 24% complete responses, 15% to PAC alone. Median duration of response (8 months) and median survival (17 months) were not superior to other reported multiagent chemotherapeutic programs. Toxicity included neutropenic fever, sepsis, renal failure, and electrolyte imbalance. Administration of sequential multiagent chemotherapy with a cisplatin-containing combination did not improve response rate, complete responses (CR), duration of response, or survival in this group of previously untreated breast cancer patients.
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Cancers (Basel)
December 2024
Department of Urology, University of Iowa, Iowa City, IA 52242, USA.
After first-line treatment failure, patients with non-muscle invasive urothelial carcinoma (NMIUC) are recommended to undergo radical cystectomy. However, those unable to pursue radical surgery or desiring bladder preservation require effective salvage therapies. Multi-agent treatment regimens are particularly useful for targeting the complex resistance mechanisms of recurrent UC.
View Article and Find Full Text PDFNeural Netw
December 2024
CAS Key Laboratory of GIPAS, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. Electronic address:
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive interactions to produce training samples, significantly increasing both the training cost and difficulty. Therefore, enhancing data efficiency is a core issue in MARL. However, in the context of MARL, agent partially observed information leads to a lack of consideration for agent interactions and coordination from an ego perspective under the world model, which becomes the main obstacle to improving the data efficiency of current proposed MARL methods.
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
IEEE Trans Cybern
November 2024
Heterogeneous multiagent systems are characterized by diverse task distributions, which are prevalent in practical scenarios, such as distributed decision making and robotic collaboration. A significant challenge in these systems is the constraint of limited observations, where each agent has access only to partial information. Many studies facilitate information exchange by employing shared parameters among agents.
View Article and Find Full Text PDFNeural Netw
November 2024
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Centralized Training with Decentralized Execution (CTDE) is a prevalent paradigm in the field of fully cooperative Multi-Agent Reinforcement Learning (MARL). Existing algorithms often encounter two major problems: independent strategies tend to underestimate the potential value of actions, leading to the convergence on sub-optimal Nash Equilibria (NE); some communication paradigms introduce added complexity to the learning process, complicating the focus on the essential elements of the messages. To address these challenges, we propose a novel method called Optimistic Sequential Soft Actor Critic with Motivational Communication (OSSMC).
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