tbuckley/cabot-search huggingface dataset:
Exploring AI-generated content and professional guidelines in cancer symptom management: A comparative analysis between ChatGPT and NCCN guidelines.
Authors: David Lazris, Yael Schenker, Teresa Thomas
Journal: Journal of Clinical Oncology (2024)

e13610 Background: Artificial intelligence (AI)-driven tools, like ChatGPT, have become widely-available sources for online health information. Limited research has explored the congruity between AI-generated content and professional treatment guidelines. This study seeks to compare recommendations for cancer-related symptoms generated from ChatGPT with guidelines from National Comprehensive Cancer Network (NCCN), a provider focused source requiring users to log in or register to access its recommendations. Utilizing a provider-focused source like NCCN serves as a benchmark to assess whether ChatGPT recommendations align with the standards typically endorsed by clinicians. Methods: We extracted treatment recommendations from four NCCN Supportive Care webpages (Cancer Pain, Antiemesis, Cancer-Related Fatigue, and Distress Management) and five subsections of the NCCN Palliative Care webpage (dyspnea, constipation, diarrhea, sleep disturbances, and anorexia/cachexia). We then entered "How can I reduce my cancer-related [symptom]" into ChatGPT 3.5 and extracted its recommendations. We calculated and compared word count and Flesch-Kincaid Grade Level readability for each NCCN and ChatGPT section. We completed a comparative content analysis focusing on recommendations for medications, consultations, and non-pharmacological strategies. Results: Across the nine NCCN Supportive Care and Palliative Care webpages, the mean word count was 2393.8 (SD=2601.4) and the mean Flesch-Kincaid Grade Level was 17.3 (SD=1.4) vs 382.4 (SD=29.6) and 11.6 (SD=0.8) for ChatGPT. The mean percent agreement between NCCN and ChatGPT recommendations was 44.6% (range 14.3%-81.8%). ChatGPT and NCCN guidelines shared fewer than half of their symptom-related recommendations in all but one section, fatigue. NCCN offered specific medication recommendations across all sections. ChatGPT's recommendations lacked the specificity observed in NCCN's guidelines including often not suggesting any medications. ChatGPT recommended specific medications in the shortness of breath and diarrhea sections that were not recommended by NCCN. NCCN's guidelines often did not include recommendations related to spirituality or palliative care consults, areas that ChatGPT addressed. Conclusions: While ChatGPT provides concise, accessible supportive care advice including many non-medical support recommendations, discrepancies with guidelines raise concerns for patient-facing symptom management recommendations. Overall, AI-generated content like ChatGPT can assist in providing preliminary information but should be researched in conjunction with comprehensive, evidence-based guidance provided by healthcare professionals. Healthcare providers should work with AI developers to ensure data sources are high-quality and accurate.
Improving dynamic prognostication of patients with hepatocellular carcinoma by node fusion algorithm equipped survival path mapping.
Authors: Lujun Shen, Yiquan Jiang, Wei-Jun Fan
Journal: Journal of Clinical Oncology (2024)

e13594 Background: Patients with hepatocellular carcinoma (HCC) always require routine surveillance and repeated treatment. Our previous work (1) developed a machine learning methodology called “Survival Path Mapping”, which converted the time series data into a cascading survival map to facilitate dynamic prognosis prediction and treatment planning. However, in previous work, the exponential growth of paths raised challenges of exhausting limited cohort and over-fitting risk. Novel algorithm enhancing data utilization in the Survival Path Mapping was in urgent need. Methods: A node fusion algorithm was designed. First, feature selection algorithm bifurcated each parent-nodes into temporary child-nodes, which constitutes temporary child-nodes set. Then, clustering was performed within temporary child-nodes set according to key features of each child-node. Finally, in each cluster, multiple child-nodes would be merged into one if there was no significant difference in survival. For nodes with bifurcation, a treatment recommendation algorithm was developed. A total of 19446 patients with newly diagnosed primary HCC at Sun Yat-sen University Cancer Center (SYSUCC) between 2008 and 2023 were retrospectively included as internal cohort (divided into internal train and holdout validation cohort); meanwhile 1135 patients from five hospitals were included as multicenter external validation cohort. Dedicated natural language processing program was developed to extract structured features from medical imaging reports. The time series data of patients were converted into data of time slices, with an interval of 3 months. Results: The original survival path mapping (raw-SP) consists of 463 different nodes, while the node fusion survival path mapping (fusion-SP) consisted of 84 nodes (81.8% decrease from raw-SP). The 3-year c-index of fusion-SP were 0.745 (95% CI 0.717-0.772), significantly better (p&lt;0.001) than that of raw-SP: 0.725 (95% CI 0.696-0.755), in the internal holdout validation. Both raw-SP and fusion-SP significantly (p &lt; 0.001) outperformed conventional BCLC and CNLC staging. In the multicenter external validation, the fusion-SP also demonstrated better performance than raw-SP and BCLC staging system. The recommended therapy calculated based fusion-SP system for patients receiving second-line or subsequent treatment had superior or non-inferior efficacy compared to BCLC guideline’s recommendation. Conclusions: The proposed node fusion algorithm solved exponential growth of paths in previous survival path algorithm, improving the accuracy and external validity of dynamic prognosis prediction for HCC patients. The fusion-SP model also retained its transparency and could be used to guide dynamic treatment planning, with its effectiveness in treatment guidance evaluated in prospective trials in the future. 1. Nat Commun 9, 2230; 2018.
A meta-analysis of models for predicting the incidence of brain metastasis in patients with lung cancer.
Authors: Sufal Chhabra, Julia Shin, Yanjia Zhang, Zhenwei Zhang, Md Ashfaq Ahmed, Atulya Aman Khosla, Nitya Batra, Mukesh Roy, Venkataraghavan Ramamoorthy, Anshul Saxena, Muni Rubens, Mohammad Muhsin Chisti
Journal: Journal of Clinical Oncology (2024)

e13613 Background: Brain metastasis (BM) represents a significant clinical challenge in the management of lung cancer, impacting patient prognosis and treatment decisions. While various predictive models have been developed to assess the risk of BM development, their performance and applicability remain under scrutiny. Understanding the accuracy and reliability of these models is crucial for informing clinical practice and improving patient care. We sought to evaluate the predictive ability of existing models predicting BM incidence in lung cancer patients, providing valuable insights into their clinical utility and areas for further refinement. Methods: Two reviewers conducted a systematic search of numerous databases, including PubMed, EMBASE, Cochrane Library, and CINAHL, and then extracted the relevant information from the eligible studies. For this study, we conducted a meta-analysis using a random-effects model. Pooled estimates were calculated for any combination of outcomes and population when at least two studies had relevant data. Study heterogeneity was assessed using I² statistics. The area under the curve (AUC) of the best model and 95% confidence intervals were set as effect measures. Additionally, funnel plots and results from meta-regression analysis were reported. Meta-analysis was performed with R software ver. 4.3.1, and quality evaluation of evidence was conducted according to the GRADE standard. Results: A total of seven studies with 535,418 participants were eligible. A random-effects model showed that models that could predict the risk of developing BM among lung cancer cases had a pooled AUC of 81% (95% CI: 74% to 87%). However, the studies had significantly higher heterogeneity, with an I 2 = 99% (p&lt;0.01). Results from meta-regression analysis, accounting for the year of publication (?=-0.013), sample size (?&lt; 0.00), number of variables (?=-0.015), and latitude (?=0.0024) did not show any statistically significant associations with a p&gt;0.05. Egger’s regression test for publication bias did not indicate small study effects (Bias estimate: 4.91, SE = 6.01, p&lt; 0.44). Conclusions: Our meta-analysis underscores the utility of existing models in predicting the incidence of BM in LC patients, with a pooled AUC of 81%. Further research aimed at refining predictive models and addressing sources of heterogeneity is imperative to enhance clinical decision-making and improve patient outcomes in this population. [Table: see text]
Non-receipt of targeted and immunotherapies in diverse patients with metastatic cancers.
Authors: Christine M Veenstra, Paul Abrahamse, Allison W. Kurian, Sarah T. Hawley, Albert Farias, Ann S. Hamilton, Kevin C Ward, Steven Jay Katz
Journal: Journal of Clinical Oncology (2024)

e13563 Background: The rapid evolution of molecularly targeted kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in the treatment of metastatic cancer, coupled with high drug costs, raises potential for unwarranted variation in patient management. We evaluated correlates of non-receipt of TKIs and ICIs in a diverse, population-based sample of patients with metastatic non-small cell lung cancer (NSCLC), kidney and bladder (GU) cancer, and melanoma. Methods: We identified 2240 patients diagnosed in 2019 with metastatic NSCLC, GU cancer, or melanoma and reported to the Georgia or Los Angeles SEER registries. Using SEER registry data, we linked all patients to a managing oncology physician. We collected data from the managing physician regarding receipt of TKIs and ICIs. We used bivariate analyses and multivariable logistic regression models to assess associations between non-receipt of TKIs and ICIs and patient-level factors. Results: We present results for a preliminary sample (N=894). About half (55%) of the patients had NSCLC, 31% had GU cancer, and 14% had melanoma. The mean age was 62 (SD 12); 59% were male; 65% were White, 16% were Black, 11% were Hispanic, and 7% were Asian; 52% had Medicare, 29% had private insurance, 9% had Medicaid, and 4% had no insurance; 89% resided in urban areas, 11% resided in rural areas. 22% lived in a census tract area with ≥20% of the population living below the poverty level. The Table shows that in bivariate analyses, non-receipt of TKIs was associated with NSCLC, older age, male sex, non-white race, and non-private or no insurance (all p&lt;0.05). After adjustment, non-receipt of TKIs was associated with NSCLC, older age, male sex, and race. Asian and Hispanic patients had lower rates of non-receipt, and Black patients had higher rates. In bivariate analyses, non-receipt of ICIs was associated with NSCLC and GU cancer (p=0.004); this association remained significant after adjustment. Conclusions: Non-receipt of TKIs was associated with important non-clinical factors including race and sex. Non-receipt of ICIs was associated only with cancer type. These findings suggest that policy-relevant factors such as out-of-pocket costs for oral cancer therapies and differential access to molecular sequencing may contribute to unwarranted variation in receipt of TKIs. [Table: see text]
Factors associated with delays in receipt of neoadjuvant chemotherapy in patients with operable breast cancer.
Authors: Margaux Wooster, David DeStephano, Melissa Beauchemin, Shikun Wang, Chin Hur, Jason Dennis Wright, Dawn L. Hershman, Melissa Kate Accordino
Journal: Journal of Clinical Oncology (2024)

e13541 Background: Delays in neoadjuvant chemotherapy initiation (NACTI) after breast cancer (BC) diagnosis have been associated with worse outcomes. We evaluated incidence and predictors of delays of NACTI among high clinical risk patients treated with NACT on a national level. Methods: We conducted a retrospective cohort study using the National Cancer Database. Eligibility included age ≥18 years, diagnosis of non-metastatic BC between 1/1/2010 – 12/31/2020, who were treated with NACT within 180 days of diagnosis. Patients who received neoadjuvant endocrine therapy were excluded. Time to chemotherapy (TTC) in days was recorded. Patients were categorized into sub-cohorts by receptor status and clinical stage: 1) Hormone Receptor positive/HER-2 negative (HR+/HER2-) and were lymph node (LN) LN+; 2) HER-2 positive (HER2+) cT2+ or LN+; and 3) triple negative BC (TNBC) cT2+ or LN+. Multivariable logistic regression, adjusted for demographic, socioeconomic and tumor characteristics, was used to identify characteristics associated with delays &gt; 60 days from diagnosis to NACTI. Multiple imputation was used for missing data. Results: We identified 145,697 eligible patients, 74% were White, 18% were Black, 3.3% were Asian, and 4.6% were Other/Unknown. Across all racial categories, 9.2% were Hispanic. Median age at diagnosis was 52 years (range 18-90), median TTC was 32 days (range 0-180), and 10% had a delay &gt; 60 days. There were 40,345 patients in the HR+/HER2- cohort, 56,868 in the HER2+ cohort, and 48,484 in the TNBC cohort; 12%, 9.0%, and 8.9% (p &lt;0.001) had a delay in NACTI, respectively. In multivariable analysis among the HR+/HER2- sub-cohort, characteristics associated with delays in NACTI included Black race (OR 1.71, 95%CI 1.57-1.86) and Hispanic ethnicity (OR 1.76, 95%CI 1.60-1.94) compared to White and non-Hispanic patients. Patients with Medicare, Medicaid, or who were uninsured were more likely to be delayed (OR 1.44 95%CI 1.30-1.58, 2.10, 95%CI 1.92-2.29, 2.28, 95%CI 1.99-2.61) compared to those with commercial insurance. Results were similar in the HER2+ and TNBC cohorts, as seen in Table. Conclusions: Among patients with operable BC who received NACT, patients who were Black, Hispanic, had Medicaid or no insurance had as high as a twofold increase in odds of a delay in NACTI. Targeted interventions to address these racial and socioeconomic disparities are necessary to ensure equitable care. Multivariable logistic regression adjusted for demographic, socioeconomic and tumor characteristics variables (n= 145,697). [Table: see text]
Barricades to aiding cascading: Patient-reported barriers to uptake in the context of clinician-facilitated cascade testing for hereditary cancer syndromes.
Authors: Isabelle Chandler, Benjamin Grant, Emily Epstein, Jesse T Brewer, Taylor Larosa, Alissa Kalyan, Sarah Schulman, Ashley Llenas, Evelyn Cantillo, Kevin Holcomb, Eloise Chapman-Davis, Ravi N. Sharaf, Melissa Kristen Frey
Journal: Journal of Clinical Oncology (2024)

e13533 Background: Cascade testing extends genetic testing to relatives of individuals with known pathogenic variants. Cascade testing can prompt lifesaving cancer surveillance, however, up to two thirds of at-risk relatives do not complete recommended genetic testing. Previous studies suggest that a clinician-mediated approach of direct relative contact can increase testing rates. As part of an ongoing clinician-facilitated cascade testing intervention, we investigated the barriers to completion of cascade testing. Methods: All patients with a known cancer-associated germline mutation presenting to a gynecologic oncology clinic from 11/2023-2/2024 were offered clinician-facilitated cascade testing, an intervention whereby the genetics team was granted permission to contact designated at-risk relatives by telephone, review the familial mutation, and assist with accessing genetic testing. Patients that declined were asked about their decision and responses were analyzed using the socio-ecological model. Results: Ninety patients were offered clinician-facilitated cascade genetic testing and 50 (55%) declined. Of the patients that declined: median age was 45 (range 23-84), 41 (82%) self-identified as non-Hispanic, 38 (76%) as White, 23 (46%) were of Ashkenazi Jewish heritage, 23 (46%) had a personal history of cancer. There were no significant differences in any of the above variables when comparing those that declined and accepted the intervention. Twenty-nine (58%) patients declined because all family members were already tested. The most common barriers were “limited access to care” (5, 10%) at the healthcare community level, and “perceived readiness of relatives” (5, 10%) at the interpersonal relationship level. While these were the most common, most barriers were at the individual level including “perceived lack of responsibility” (3, 6%), “limited knowledge” (2, 4%), and “emotional burden” (2, 4%) (Table). Stated barriers were not associated with any demographical group. Conclusions: Although clinician-facilitated cascade testing has been shown to increase rates of relative testing uptake, several barriers persist at all levels of the socio-ecological model. While facilitated interventions should be individualized, our findings suggest that future interventions should focus efforts at all socio-ecological levels. Clinical trial information: NCT04613440 . [Table: see text]
Application of multidisciplinary team model based on full-course management in health promotion of patients with breast cancer.
Authors: Xiuyuan Xie, Fenghe Liu, Yujie Fei, Haiping Xu
Journal: Journal of Clinical Oncology (2024)

e13546 Background: To explore the application effectiveness of the multidisciplinary team model based on full-course management, in advancing the health promotion of breast cancer patients. Methods: This research involved a cohort of 84 breast cancer patients, recruited from the First Affiliated Hospital of Nanjing Medical University, spanning December 2022 and June 2023. These participants were divided into a control group and an intervention group using a convenience sampling method. The control group accepted routine nursing care, while the intervention group was treated with the multidisciplinary team approaches based on full-course management. These approaches included informal pre-admission interventions and multi-dimensional improvements to peri-operative patient comfort, integrated regimens combining Chinese and Western medicine to alleviate chemotherapy complications, diversified and extended nursing care focused on improving patients' quality of life, and facilitating physical and psychological rehabilitation. The two groups of patients were evaluated in satisfaction, comfort, quality of life, psychological state and other aspects. The results were evaluated by self-made satisfaction scale, breast cancer complications during chemotherapy questionnaire, Kolcaba Comfort Scale, Quality of Life Scale (QLQ-C30), self-rating Anxiety Scale (SAS) and self-rating Depression Scale (SDS). Patient outcomes were assessed in terms of satisfaction, comfort, quality of life, and psychological state. The results were evaluated by the self-made satisfaction scale, the breast cancer chemotherapy complications questionnaire, the Kolcaba Comfort Scale, the Quality of Life Scale (QLQ-C30), the Self-Rating Anxiety Scale (SAS), and the Self-Rating Depression Scale (SDS). Results: The intervention group showed statistically significant improvements in patient satisfaction, comfort and quality of life scores compared to the control group (P&lt;0.05). Additionally, they exhibited a lower incidence of chemotherapy complications, severe anxiety and depression during the extended care period (P&lt;0.05). Conclusions: The multi-disciplinary team model based on the full-course management demonstrated significant clinical benefits in the application of the full-course management of breast cancer patients. It effectively reduced chemotherapy-related complications, improved peri-operative comfort, decreased the incidence of severe anxiety and depression during the period of extended care, improved the physical and mental well-being of patients, leading to greater satisfaction and an enhanced quality of life.
AI-based quantification of TILs using hematoxylin and eosin and immunohistochemistry-stained slides in triple-negative breast cancer.
Authors: Takuma Kobayashi, Nobumoto Tomioka, Kanako C Hatanaka, Teppei Konishi, Mateusz Grynkiewicz, Daisuke Komura, Shumpei Ishikawa, Kenichi Watanabe, Masato Takahashi, Yutaka Hatanaka
Journal: Journal of Clinical Oncology (2024)

e13608 Background: The importance of accurately understanding the status of tumor-infiltrating lymphocytes (TILs) in the preoperative chemotherapy context, for triple-negative breast cancer is underscored, highlighting the dual role of TILs in gauging treatment efficacy and prognostication. However, the difficulty in quantitatively and objectively assessing TILs in clinical practice is acknowledged, noting the barrier to its adoption as a standard diagnostic end-point. This study proposes a novel AI-based method to overcome these challenges, by quantitatively assessing TILs using hematoxylin and eosin (H&amp;E) and immunohistochemistry (IHC) staining, and exploring their prognostic relationship. Methods: In this study, 68 cases of breast cancer with axillary lymph node metastasis at the time of resection, were selected from a total of 3,902 resections conducted between 2002 and 2016. To quantitatively assess TILs, we developed a quantitative assessment method, integrating an AI-based epithelial detection model and non-rigid image registration techniques to align H&amp;E and IHC-stained images for precise measurement of immune markers (CD3, CD8, FOXP3, PD-L1) within the tumor stroma. Using an AI-based model trained on 12,273 annotated epithelial images, the stroma, excluding the epithelium, was identified. The tumor bed was also defined by a pathologist. Finally, we assessed the prognostic significance of the TILs for disease-free survival (DFS) through ROC analysis and log-rank tests. Results: Optimal thresholds for CD3 (0.152 mm²), CD8 (4.8 mm²), FOXP3 (0.25 mm²), and PD-L1 (0.115 mm²) were identified, with corresponding AUCs of 0.678, 0.625, 0.746, and 0.659. Patients with cell counts or areas above these thresholds demonstrated improved DFS (P&lt;0.05 for all markers). Conclusions: These findings suggest that the quantification of immune cells within the tumor bed provides reliable prognostic indicators for recurrence. The AI-based TILs quantification offers a significant advancement in the prognostic assessment of triple-negative breast cancer. In addition, it improves the objectivity and clinical utility of pathological evaluations. This precise measurement of TILs could be transformative in patient prognostication and the formulation of treatment plans.