Patient age is related to decision-making, treatment selection, and perceived quality of life in breast cancer survivors

BACKGROUND: Patients with breast cancer must choose among a variety of treatment options when first diagnosed. Patient age, independent of extent of disease, is also related to quality of life. This study examined the impact of patient age on treatment selected, factors influencing this selection, and perceived quality of life. METHODS: A 62-question survey evaluating breast cancer treatment and quality of life was mailed to breast cancer survivors. Responses were stratified by age (<50, 50-65, >65 years) and extent of disease. RESULTS: Of the 1,131 surveys mailed, 402 were included for analysis. There were 104, 179, and 119 women aged <50, 50-65, and >65 years, respectively. The median patient age was 58 years, and the average interval from diagnosis to survey participation was 31.5 months. CONCLUSIONS: Young women were more likely to have undergone aggressive therapies and had better physical functioning than old women. Old patients reported good quality of life and body image. Clinicians should consider patient age when discussing breast cancer treatment options

The Pervasive Crisis of Diminishing Radiation Therapy Access for Vulnerable Populations in the United States—Part 4: Appalachian Patients

Purpose: Compared with the rest of the United States, the population of Appalachia has lower education levels, higher rates of poverty, and limited access to health care. The presence of disparities in radiation therapy (RT) access for Appalachian patients with cancer has rarely been examined. Methods and materials: The National Cancer Institute initiatives toward addressing disparities in treatment access for rural populations were examined. An extensive literature search was undertaken for studies investigating RT access disparities in Appalachian patients, beginning with the most common cancers in these patients (lung, colorectal, and cervical). Results: Although the literature investigating RT access disparities in Appalachia is relatively sparse, studies examining lung, colorectal, cervical, prostate, head and neck, breast, and esophageal cancer, as well as lymphoma, indicate an unfortunate commonality in barriers to optimal RT access for Appalachian patients with cancer. These barriers are predominantly socioeconomic in nature (low income and lack of private insurance) but are exacerbated by paucities in both the number and quality of radiation centers that are accessible to this patient population. Conclusions: Regardless of organ system, there are significant barriers for Appalachian patients with cancer to receive RT. Such diminished access is alarming and warrants resources devoted to addressing these disparities, which often go overlooked because of the assumption that the overall wealth of the United States is tangibly applicable to all of its citizens. Without intelligently targeted investments of time and finances in this arena, there is great risk of exacerbating rather than alleviating the already heavy burden facing Appalachian patients with cancer

Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics

We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs. We developed an exam consisting of 100 radiation oncology physics questions based on our expertise at Mayo Clinic. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. ChatGPT (GPT-4) outperformed all other LLMs as well as medical physicists, on average. The performance of ChatGPT (GPT-4) was further improved when prompted to explain first, then answer. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups. In evaluating ChatGPTs (GPT-4) deductive reasoning ability using a novel approach (substituting the correct answer with "None of the above choices is the correct answer."), ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote. This study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. Methods: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.Comment: 9 figure

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

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy

Purpose: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. Results: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s

Postoperative Cavity Stereotactic Radiosurgery for Brain Metastases

During the past decade, tumor bed stereotactic radiosurgery (SRS) after surgical resection has been increasingly utilized in the management of brain metastases. SRS has risen as an alternative to adjuvant whole brain radiation therapy (WBRT), which has been shown in several studies to be associated with increased neurotoxicity. Multiple recent articles have shown favorable local control rates compared to those of WBRT. Specifically, improvements in local control can be achieved by adding a 2 mm margin around the resection cavity. Risk factors that have been established as increasing the risk of local recurrence after resection include: subtotal resection, larger treatment volume, lower margin dose, and a long delay between surgery and SRS (&gt;3 weeks). Moreover, consensus among experts in the field have established the importance of (a) fusion of the pre-operative magnetic resonance imaging scan to aid in volume delineation (b) contouring the entire surgical tract and (c) expanding the target to include possible microscopic disease that may extend to meningeal or venous sinus territory. These strategies can minimize the risks of symptomatic radiation-induced injury and leptomeningeal dissemination after postoperative SRS. Emerging data has arisen suggesting that multifraction postoperative SRS, or alternatively, preoperative SRS could provide decreased rates of radiation necrosis and leptomeningeal disease. Future prospective randomized clinical trials comparing outcomes between these techniques are necessary in order to improve outcomes in these patients

An unusual case of isolated, serial metastases of gallbladder carcinoma involving the chest wall, axilla, breast and lung parenchyma

In the English literature, only 9 cases of adenocarcinoma of the gallbladder with cutaneous metastasis have been reported so far. One case of multiple cutaneous metastases along with deposits in the breast tissue has been reported. We present a case of incidental metastatic gallbladder carcinoma with no intra-abdominal disease presenting as a series of four isolated cutaneous right chest wall, axillary nodal, breast and pulmonary metastases following resection and adjuvant chemoradiation for her primary tumor. In spite of the metastatic disease coupled with the aggressive nature of the cancer, this patient reported that her energy level had returned to baseline with a good appetite and a stable weight indicating a good performance status and now is alive at 25 months since diagnosis. Her serially-presented, oligometastatic diseases were well-controlled by concurrent chemoradiation and stereotactic radiation therapy. We report this case study because of its rarity and for the purpose of complementing current literature with an additional example of cutaneous metastasis from adenocarcinoma of the gallbladder