46 research outputs found
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, >65Â 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 >65Â years, respectively. The median patient age was 58Â years, and the average interval from diagnosis to survey participation was 31.5Â 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 (>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
Doxepin for radiation therapy-induced mucositis pain in the treatment of oral cancers
Radiotherapy (RT), an integral part of the oncologic treatment for patients with head and neck cancer, can cause adverse side effects such as oral mucositis (OM). Pain from OM can impact a patient’s quality of life and interrupt RT treatment schedules, which decreases the probability for achieving cancer cure. Conventionally, RT-induced OM pain is treated with analgesics and/or mouthwash rinses. Doxepin, a traditional tricyclic antidepressant with analgesic and anesthetic properties when applied topically to the mucosa, has been shown to lower OM pain in multiple single-arm trials (Epstein et al.) and more recently, in a placebo-controlled crossover study (Leenstra and Miller et al.). Currently, a placebo-controlled study (Sio and Miller et al.) using doxepin for esophagitis pain caused by RT to the thorax is underway. Doxepin will also be further compared with magic mouthwash and a placebo solution in a three-arm trial (Miller and Sio et al.) with head and neck cancer patients with OM pain caused by RT. Doxepin may represent a new standard for treating RT-induced OM pain in the future