BACKGROUND: Artificial Intelligence (AI) has emerged as a promising tool in the delivery of health care. ChatGPT-4.0 (OpenAI, San Francisco, California) and Llama 2 (Meta, Menlo Park, CA) have each gained attention for their use in various medical applications.
OBJECTIVE: This study aims to evaluate and compare the effectiveness of ChatGPT-4.0 and Llama 2 in assisting with complex clinical decision making in the diagnosis and treatment of thyroid carcinoma.
METHODS: We reviewed the National Comprehensive Cancer Network® (NCCN) Clinical Practice Guidelines for the management of thyroid carcinoma and formulated up to 3 complex clinical questions for each decision-making page. ChatGPT-4.0 and Llama 2 were queried in a reproducible manner. The answers were scored on a Likert scale: 5) Correct; 4) correct, with missing information requiring clarification; 3) correct, but unable to complete answer; 2) partially incorrect; 1) absolutely incorrect. Score frequencies were compared, and subgroup analysis was conducted on Correctness (defined as scores 1-2 vs 3-5) and Accuracy (scores 1-3 vs 4-5).
RESULTS: In total, 58 pages of the NCCN Guidelines® were analyzed, generating 167 unique questions. There was no statistically significant difference between ChatGPT-4.0 and Llama 2 in terms of overall score (Mann-Whitney U-test; Mean Rank = 160.53 vs 174.47, P = 0.123), Correctness (P = 0.177), or Accuracy (P = 0.891).[Formula: see text].
CONCLUSIONS: ChatGPT-4.0 and Llama 2 demonstrate a limited but substantial capacity to assist with complex clinical decision making relating to the management of thyroid carcinoma, with no significant difference in their effectiveness.

This is an executive summary of the most recent American Society for Radiation Oncology (ASTRO) guidelines on use of partial breast irradiation in early-stage breast cancer.In the conscientious pursuit of \"right-sizing\" the management of patients with early-stage breast cancer, there has been an emphasis on judicious de-escalation of therapy. A component of this paradigm shift is partial breast irradiation (PBI), an approach characterized by targeted radiation therapy (RT) to lumpectomy cavity margins rather than to the whole breast (i.e., whole breast irradiation [WBI]) after breast conservation surgery (BCS). The American Society for Radiation Oncology (ASTRO) recently completed a revision of its evidence-based guidelines for the application of PBI.1To accomplish this, recent PBI data were reviewed by panel members, including representatives of the American Society for Radiation Oncology (ASTRO), in collaboration with the American Society of Clinical Oncology (ASCO), and the Society of Surgical Oncology (SSO), which provided representatives and peer reviewers. The guideline was approved by the ASTRO Board of Directors and endorsed by the Canadian Association of Radiation Oncology, European Society for Radiotherapy and Oncology, Royal Australian and New Zealand College of Radiologists, and the Society of Surgical Oncology.The recommendations focused on indications for PBI as an alternative to WBI and technical considerations specific to PBI. This editorial provides a summary and comments on the updated ASTRO PBI guidelines, offering insights into the implications of these findings for clinical practice and multidisciplinary decision-making while underscoring technical considerations for optimal incorporation of PBI into patient care.

BACKGROUND: Artificial intelligence continues to play an increasingly important role in modern health care. ChatGPT-3.5 (OpenAI, San Francisco, CA) has gained attention for its potential impact in this domain.
OBJECTIVE: To explore the role of ChatGPT-3.5 in guiding clinical decision-making specifically in the context of pancreatic adenocarcinoma and to assess its growth over a period of time.
METHODS: We reviewed the National Comprehensive Cancer Network® (NCCN) Clinical Practice Guidelines for the Management of Pancreatic Adenocarcinoma and formulated a complex clinical question for each decision-making page. ChatGPT-3.5 was queried in a reproducible fashion. We scored answers on the following Likert scale: 5) Correct; 4) Correct, with missing information requiring clarification; 3) Correct, but unable to complete answer; 2) Partially incorrect; 1) Absolutely incorrect. We repeated this protocol at 3-months. Score frequencies were compared, and subgroup analysis was conducted on Correctness (defined as scores 1-2 vs 3-5) and Accuracy (scores 1-3 vs 4-5).
RESULTS: In total, 50-pages of the NCCN Guidelines® were analyzed, generating 50 complex clinical questions. On subgroup analysis, the percentage of Acceptable answers improved from 60% to 76%. The score improvement was statistically significant (Mann-Whitney U-test; Mean Rank = 44.52 vs 56.48, P = .027).
CONCLUSIONS: ChatGPT-3.5 represents an interesting but limited tool for assistance in clinical decision-making. We demonstrate that the platform evolved, and its responses to our standardized questions improved over a relatively short period (3-months). Future research is needed to determine the validity of this tool for this clinical application.

Deaths from the majority of cancers are falling globally, but the incidence and mortality from hepatocellular carcinoma (HCC) is increasing in the United Kingdom and in other Western countries. HCC is a highly fatal cancer, often diagnosed late, with an incidence to mortality ratio that approaches 1. Despite there being a number of treatment options, including those associated with good medium to long-term survival, 5-year survival from HCC in the UK remains below 20%. Sex, ethnicity and deprivation are important demographics for the incidence of, and/or survival from, HCC. These clinical practice guidelines will provide evidence-based advice for the assessment and management of patients with HCC. The clinical and scientific data underpinning the recommendations we make are summarised in detail. Much of the content will have broad relevance, but the treatment algorithms are based on therapies that are available in the UK and have regulatory approval for use in the National Health Service.

Based on the NCCN Guidelines for Soft Tissue Sarcoma (STS), treatment of extremity STS (ESTS) includes radiation therapy (RT) and surgical resection for tumors that are high-grade and >5 cm. ​​The aim of this study was to describe the association between neighborhood socioeconomic status (nSES), concordance with NCCN Guidelines recommendations, and outcomes in patients with ESTS.
Patients with ESTS diagnosed from 2006 through 2018 were identified in SEER registries. The analytic cohort was restricted to patients with high-grade tumors >5 cm without nodal or distant metastases who received limb-sparing surgery. Patient demographics and tumor characteristics associated with receipt of RT were analyzed using adjusted regression analyses. Kaplan-Meier curves and adjusted accelerated failure time models were used to examine disparities in cancer-specific survival.
Of 2,249 patients, 29.0% (n=648) received neoadjuvant RT, 49.7% (n=1,111) received adjuvant or intraoperative RT, and 21.3% (n=476) did not receive RT. In adjusted analyses, lower nSES was associated with lower likelihood of receiving RT (odds ratio, 0.70 [95% CI, 0.57-0.87]; P<.001). Low nSES was associated with worse cancer-specific survival (hazard ratio, 1.19 [95% CI, 1.01-1.40]; P=.04). Race and ethnicity were not significant predictors of receipt of RT or cancer-specific survival in the fully adjusted models.
Patients from lower nSES areas were less likely to receive NCCN Guideline-recommended RT for their ESTS and had worse cancer-specific survival. Efforts to better define and resolve disparities in the treatment and survival of patients with ESTS are warranted.

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The Society of Surgeons of Pakistan and The Society of Surgical Oncology of Pakistan with factions from various major centres comprising of surgical oncology, medical and radiation oncology collaborated to reach consensus on breast cancer management guidelines and a framework of \"good practice\" minimum standards of care. The aim of the task force was to enhance treatment standards, which have a direct correlation with improving patient mortality and morbidity and long-term survival whilst taking into consideration economic limitations of access to leading centers of excellence as well as minimum expertise required in health care. These multidisciplinary guidelines, whilst not exhaustive, aim to provide an algorithm of care for breast cancer patients at tertiary care centres and district level hospitals to provide most appropriate treatment.

OBJECTIVE: To develop recommendations for germline mutation testing for patients with breast cancer.
METHODS: An ASCO-Society of Surgical Oncology (SSO) panel convened to develop recommendations based on a systematic review and formal consensus process.
RESULTS: Forty-seven articles met eligibility criteria for the germline mutation testing recommendations; 18 for the genetic counseling recommendations.
CONCLUSIONS: BRCA1/2 mutation testing should be offered to all newly diagnosed patients with breast cancer ≤65 years and select patients >65 years based on personal history, family history, ancestry, or eligibility for poly(ADP-ribose) polymerase (PARP) inhibitor therapy. All patients with recurrent breast cancer who are candidates for PARP inhibitor therapy should be offered BRCA1/2 testing, regardless of family history. BRCA1/2 testing should be offered to women who develop a second primary cancer in the ipsilateral or contralateral breast. For patients with prior history of breast cancer and without active disease, testing should be offered to patients diagnosed ≤65 years and selectively in patients diagnosed after 65 years, if it will inform personal and family risk. Testing for high-penetrance cancer susceptibility genes beyond BRCA1/2 should be offered to those with supportive family histories; testing for moderate-penetrance genes may be offered if necessary to inform personal and family cancer risk. Patients should be provided enough pretest information for informed consent; those with pathogenic variants should receive individualized post-test counseling. Variants of uncertain significance should not impact management, and patients with such variants should be followed for reclassification. Referral to providers experienced in clinical cancer genetics may help facilitate patient selection and interpretation of expanded testing, and provide counseling of individuals without pathogenic germline variants but with significant family history.Additional information is available at www.asco.org/breast-cancer-guidelines.

BACKGROUND: Using real working examples, we provide strategies and address challenges in linear and logistic regression to demonstrate best practice guidelines and pitfalls of regression modeling in surgical oncology research.
METHODS: To demonstrate our best practices, we reviewed patients who underwent tissue expander breast reconstruction between 2019 and 2021. We assessed predictive factors that affect BREAST-Q Physical Well-Being of the Chest (PWB-C) scores at 2 weeks with linear regression modeling and overall complications and malrotation with logistic regression modeling. Model fit and performance were assessed.
RESULTS: The 1986 patients were included in the analysis. In linear regression, age [β = 0.18 (95% CI: 0.09, 0.28); p < 0.001], single marital status [β = 2.6 (0.31, 5.0); p = 0.026], and prepectoral pocket dissection [β = 4.6 (2.7, 6.5); p < 0.001] were significantly associated with PWB-C at 2 weeks. For logistic regression, BMI [OR = 1.06 (95% CI: 1.04, 1.08); p < 0.001], age [OR = 1.02 (1.01, 1.03); p = 0.002], bilateral reconstruction [OR = 1.39 (1.09, 1.79); p = 0.009], and prepectoral dissection [OR = 1.53 (1.21, 1.94); p < 0.001] were associated with increased likelihood of a complication.
CONCLUSIONS: We provide focused directives for successful application of regression techniques in surgical oncology research. We encourage researchers to select variables with clinical judgment, confirm appropriate model fitting, and consider clinical plausibility for interpretation when utilizing regression models in their research.

Current prognostic factors for endometrial cancer are not sufficient to predict recurrence in early stages. Treatment choices are based on the prognostic factors included in the risk classes defined by the ESMO-ESGO-ESTRO (European Society for Medical Oncology-European Society of Gynaecological Oncology-European Society for Radiotherapy and Oncology) consensus conference with the new biomolecular classification based on POLE, TP53, and microsatellite instability status. However, a minority of early stage cases relapse regardless of their low risk profiles. Integration of the immune context status to existing molecular based models has not been fully evaluated. This study aims to investigate whether the integration of the immune landscape in the tumor microenvironment could improve clinical risk prediction models and allow better profiling of early stages.
Leveraging the potential of in silico deconvolution tools, we estimated the relative abundances of immune populations in public data and then applied feature selection methods to generate a machine learning based model for disease free survival probability prediction.
We included information on International Federation of Gynecology and Obstetrics (FIGO) stage, tumor mutational burden, microsatellite instability, POLEmut status, interferon γ signature, and relative abundances of monocytes, natural killer cells, and CD4+T cells to build a relapse prediction model and obtained a balanced accuracy of 69%. We further identified two novel early stage profiles that undergo different pathways of recurrence.
This study presents an extension of current prognostic factors for endometrial cancer by exploiting machine learning models and deconvolution techniques on available public biomolecular data. Prospective clinical trials are advisable to validate the early stage stratification.