RadOnc-GPT: A Large Language Model for Radiation Oncology

This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on a large dataset of radiation oncology patient records and clinical notes from the Mayo Clinic in Arizona. The model employs instruction tuning on three key tasks - generating radiotherapy treatment regimens, determining optimal radiation modalities, and providing diagnostic descriptions/ICD codes based on patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT outputs to general large language model outputs showed that RadOnc-GPT generated outputs with significantly improved clarity, specificity, and clinical relevance. The study demonstrated the potential of using large language models fine-tuned using domain-specific knowledge like RadOnc-GPT to achieve transformational capabilities in highly specialized healthcare fields such as radiation oncology

Artificial General Intelligence for Radiation Oncology

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale

Stable Low-Rank CP Decomposition for Compression of Convolutional Neural Networks Based on Sensitivity

Modern convolutional neural networks (CNNs) play a crucial role in computer vision applications. The intricacy of the application scenarios and the growing dataset both significantly raise the complexity of CNNs. As a result, they are often overparameterized and have significant computational costs. One potential solution for optimizing and compressing the CNNs is to replace convolutional layers with low-rank tensor decomposition. The most suitable technique for this is Canonical Polyadic (CP) decomposition. However, there are two primary issues with CP decomposition that lead to a significant loss in accuracy. Firstly, the selection of tensor ranks for CP decomposition is an unsolved issue. Secondly, degeneracy and instability are common problems in the CP decomposition of contractional tensors, which makes fine-tuning the compressed model difficult. In this study, a novel approach was proposed for compressing CNNs by using CP decomposition. The first step involves using the sensitivity of convolutional layers to determine the tensor ranks for CP decomposition effectively. Subsequently, to address the degeneracy issue and enhance the stability of the CP decomposition, two novel techniques were incorporated: optimization with sensitivity constraints and iterative fine-tuning based on sensitivity order. Finally, the proposed method was examined on common CNN structures for image classification tasks and demonstrated that it provides stable performance and significantly fewer reductions in classification accuracy

Particle Pollution Estimation Based on Image Analysis.

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction

Economic stability analysis of blue carbon cooperation in the South China sea region using evolutionary game model with Weber's law

The political environment of the South China Sea Region (SCSR) has gradually stabilized, such that regional cooperation in the preservation of marine resources seems realistic. Blue carbon international cooperation is an important solution to the problem of global warming, which has a large number of economic and political attributes. As a region that has incredibly abundant blue carbon resources, further cooperation among SCSR governments would present the opportunity to establish meaningful economic and environmental protections that would promote peaceful blue carbon development of this region. To examine the feasibility of such an undertaking, we leverage the imitator's dynamic game as a research method and introduce Weber's law to examine the subjective psychological factors (i.e., biases) of participants in qualifying the economic stability of blue carbon cooperation in the SCSR. The results suggest that the economic stability of blue carbon cooperation correlates to Weber's coefficient and the income produced by the different strategies. Based on these findings, we discussed policy recommendations to promote the sustainable economic development of SCSR

Evaluation of Plan Robustness Using Hybrid Intensity-Modulated Radiotherapy (IMRT) and Volumetric Arc Modulation Radiotherapy (VMAT) for Left-Sided Breast Cancer

Purpose: We aim to evaluate the robustness of multi-field IMRT and VMAT plans to target motion for left-sided BC radiotherapy. Methods: The 7-field hybrid IMRT (7F-H-IMRT) and 2-arc VMAT (2A-VMAT) plans were generated for ten left-sided BC patients. Shifts of 3 mm, 5 mm, and 10 mm in six directions were introduced and the perturbed dose distributions were recalculated. The dose differences (∆D) of the original plan and perturbed plan corresponded to the plan robustness for the structure. Results: Higher ∆D98%, ∆D95%, and ∆Dmean of CTV were observed in 2A-VMAT plans, which induced higher tumor control probability reductions. A higher ∆Dmean of CTV Boost was found in 7F-H-IMRT plans despite lower ∆D98% and ∆D95%. Shifts in the S-I direction exerted the largest effect on CTV and CTV Boost. Regarding OARs, shifts in R, P, and I directions contributed to increasing the received dose. The 2A-VMAT plans performed better dose sparing, but had a higher robustness in a high-dose volume of the left lung and heart. The 2A-VMAT plans decreased the max dose of LAD but exhibited lower robustness. Conclusion: The 2A-VMAT plans showed higher sensitivity to position deviation. Shifts in the S-I direction exerted the largest effect for CTV and CTV Boost

Distribution Patterns and Determinants of Invasive Alien Plants in China

In recent years, invasive alien plants (IAPs) have caused serious ecological disasters and economic losses in China. This study combined three IAP species richness-related indices (species richness of IAPs, first records of IAPs, and the relative species richness of IAPs), as well as indices reflecting distribution and dispersal patterns (average similarity coefficient of IAPs) and invasiveness (average risk score of IAPs), to conduct an integrated regional-invasion risk assessment based on the principal component analysis (PCA) method. Partial least-squares (PLS) regression was conducted to explore the explanatory power of 12 environmental and anthropogenic factors on different invasion indices. The results indicated that coastal provinces and Yunnan had high IAP introduction risk, as well as high synthetic-risk scores. The dispersal of IAPs in mid-latitude provinces should be particularly prevented. For species richness of IAPs, more environmental factors with variable importance for the project (VIP) values higher than 1 were retained in the optimal model, reflecting the importance of environmental filtering on IAPs. Visitors were the most important predictor for first records of IAPs. Compared to species richness (R2 = 79.5%), first records were difficult to predict (R2 = 60.4%) and were influenced by anthropogenic factors. There was spatial distribution congruence of various families of IAPs. Generally, the correlations of the residuals of species richness were still significant, with 0.421 (p < 0.05) as the lowest Pearson correlation coefficient, which indicated that external factors could not fully explain the spatial distribution congruence. These findings could enrich the relevant research on IAP invasion mechanisms and provide suggestions for regional IAP detection and response