tbuckley/cabot-search huggingface dataset:
Demographics and outcomes of cancer patients on Medicaid versus private insurance admitted to the hospital in a national sample.
Authors: Omar Ibrahim, Pierre Alexander Rodriguez Alarcon, Lina James George, Shweta Gupta, Maria Cristina Cuartas Mesa, Alexandra Sueldo, Juana Yamileth Fonseca
Journal: Journal of Clinical Oncology (2025)

e13823 Background: Cancer is the second leading cause of death in the United States and one of the leading causes of hospital admissions and disability in the United States. There are also reports of rising incidence of cancer in younger population and rising costs of cancer care. We undertook this study to understand the differences in outcomes in patients with cancer admitted to the hospital on Medicaid versus Private insurance using a national sample. Methods: Methods: Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) was queried to identify all admissions in adult patients &lt; 65 years old with cancer between 2016-2021. The patients on Medicaid and Private insurance were identified from this sample and compared for socio-demographic differences, medical comorbidities, all-cause mortality, mean length of stay (LOS) and mean total hospital charges (THC). Statistics were performed using the t-test, univariate and multinomial logistic regression. Results: Results: A total of 5,478,819 admissions occurred in patients with cancer of which 1,284,380 were on Medicaid while 2,843,950 were on Private Insurance. The Medicaid patients were significantly younger (50.9 vs 52.9 years, p &lt; 0.001), more women (50.4% vs 49.6%, p &lt; 0.0001) and more likely to be Black (22.7 vs 11.3%), Hispanic (18.1 vs 8.6%) and less likely to be White (50.4 vs 71.9), all p &lt; 0.0001. Medicaid patients were more likely to be treated at rural and urban non-teaching hospitals compared to Private Insurance (19.2 vs 17.2%, p &lt; 0.0001) and were two times more likely to be from lowest quartile income zip codes (40.2% vs 20.1%, &lt; 0.0001). Medicaid patients also had a higher Charlson Comorbidity index with 75% having index of 3 or higher compared to 64% for Private Insurance (p &lt; 0.0001). Patients on Medicaid had a high mean LOS (7.1 vs 5.9 days, p &lt; 0.0001) and total charges ($87,470 vs $85,163, p &lt; 0.0001) both of which remained significant after adjustment for all demographics and comorbidities. There were more deaths in Medicaid group (5.1% vs 4.2%, p &lt; 0.0001) that also remained significant after adjustment for demographics and comorbid conditions including hypertension, diabetes, COPD, CKD, dialysis dependence, dyslipidemia, heart failure and obesity. Conclusions: Medicaid shoulders a significantly larger burden of sicker, poorer and younger cancer patients compared to Private Insurance. Such patients have higher cost of care and poorer outcomes when admitted to the hospital. More studies are needed to see why cancer patients on Medicaid have poorer outcomes and if they require more supportive services to improve their cancer outcomes.
Impact of surgical delay beyond 8 weeks on tumor progression and lymph node involvement in early breast cancer: Insights from the KHRONOS Project.
Authors: Pablo Flores Paco, Luis Perez-Bartivas, Lucia Navarro, David Ponferrada, Marta Martínez, Francisco Ruiz-Portero, Daniel Sánchez-Miranda, Pedro Sánchez Mauriño, Javier Cejas, Juan Adrián Camús, Natalia Rangel, Alicia Vargas, Pilar Rioja, Amalia Palacios, Marina Alvarez, Enrique Aranda, Juan De La Haba
Journal: Journal of Clinical Oncology (2025)

e12552 Background: Recent evidence highlights a potential negative impact of delaying surgery for early breast cancer (BC) beyond 8 weeks; however, the interplay between tumor biology and timing remains underexplored. This study investigates the role of surgical delay on tumor progression and lymph node (LN) involvement, emphasizing the biological heterogeneity of BC. Methods: This retrospective cohort study analyzed data from 924 early-stage BC patients (pts) (2015-2023). 267 pts underwent PAM50 testing to determine their intrinsic subtype. Patients had no initial radiological LN involvement and underwent primary surgery. Tumor progression and nodal status transition (N0 to N+) were analyzed based on surgical timing (≤ 8 weeks vs. &gt; 8 weeks). Chi-square and ANOVA tests were employed to assess associations between clinicopathologic variables and outcomes. Results: 664 pts in our cohort (71.9%) underwent breast-conserving surgery, while 260 pts (28.1%) were treated with mastectomy. A total of 204 pts (22.08%) had a G3 tumor grade. The majority of tumors (784 pts; 84.89%) were smaller than 5 cm, as determined by mammography. Among the 267 pts who underwent the PAM50 test, 260 pts were classified within the luminal intrinsic subtype (141 pts as Luminal A and 120 pts as Luminal B). No significant differences were observed in tumor size progression or lymph node involvement between pts who underwent surgery within 8 weeks and those who experienced delays (p &gt; 0.05). Subgroup analysis revealed that pts with G3 tumors had a higher likelihood of nodal progression when surgery was delayed (37.8% vs. 23.8%, p = 0.034). Additionally, the Luminal B subtype demonstrated increased nodal involvement associated with surgical delays (7,1% vs. 20,4%, p = 0.037). Conclusions: The subgroup of high-risk pts (G3) and luminal intrinsic subtype B may be associated with increased lymph node involvement following a surgical delay of &gt; 8 weeks. These findings emphasize the importance of tumor biology in surgical prioritization and highlight the necessity for strategies to minimize delays in high-risk pts.
Validation of generative AI models’ performance on patient education quizzes: Hormone-related symptoms of cancer care.
Authors: Claire Dvorak, Jeffrey R Smith, Nikita C. Shah, Patrick Kelly, Tomas Dvorak
Journal: Journal of Clinical Oncology (2025)

e13881 Background: To evaluate the clinical reliability and accuracy of generative AI models when answering patient-level educational quizzes on hormone-related symptoms of cancer care during survivorship. This assessment aimed to measure the consistency of AI responses and identify areas of potential improvement. Methods: Two 10-question patient-level gender-specific quizzes (one for men and one for women) were compiled from the highest-quality questions generated by five generative AI models (GPT-o1, Claude3.5 Sonnet, Grok2, Gemini1.5, and DeepSeek). Questions were selected based on their relevance, clarity, and clinical appropriateness, with each quiz covering key domains: (1) sex hormones and cancer, (2) type and timing of symptoms, (3) assessment of symptoms, (4) treatment of hot flashes, (5) treatment of gynecomastia (men only), and (6) treatment of urogenital problems (women only). Each quiz was administered to all five AI models in five independent rounds (50 total attempts across both quizzes). Results: Overall, the models demonstrated high accuracy on both quizzes, with the majority achieving 100% across all rounds. On the men’s quiz, GPT-o1, Grok2, and DeepSeek consistently scored 100% across all five rounds. However, Claude3.5 scored 9/10 in Round 1 of 5, and Gemini1.5 scored 9/10 in Round 5 of 5. Both models incorrectly answered the same question in that particular Round: "Which treatments are available for enlarged male breast tissue (gynecomastia)?" The correct answer was, "d) All of the above," but instead Claude3.5 answered “both b) surgery and c) tamoxifen”), and Gemini1.5 answered “b) surgery in most cases”, omitting the other correct options. On the women’s quiz, all five models achieved perfect scores (100%) across all rounds. The aggregate accuracy was 99.6% (498/500 questions). Conclusions: Generative AI models exhibited high reliability and accuracy when tested on patient education quizzes derived from national clinical guidelines, particularly on questions addressing female-specific hormone-related symptoms. Errors on the men’s quiz highlight the need for refinement in multi-option clinical reasoning. These findings support the potential for generative AI models to serve as reliable and consistent tools for reinforcing patient education, with additional real-world patient testing and iteration necessary to optimize their clinical utility.
The landscape ecology of DCIS: Microenvironmental drivers of recurrence and progression to breast cancer.
Authors: Luis Humberto Cisneros, Ryan Michael Carr, Yinyin Yuan, Carlo Maley
Journal: Journal of Clinical Oncology (2025)

e12568 Background: Modern preventive screening have made it possible to identify ductal carcinoma in situ (DCIS). Progression of DCIS to invasive disease is known to be driven by somatic evolution and affected by clinical interventions. Yet studies indicate that only 20–30% of the cases of DCIS will progress further if not treated, implying that a large portion of patients are over treated. Recognizing which pre-cancerous tumors are likely to progress to cancer or recur as DCIS after initial treatment is of great importance to optimize treatment options, lower associated risks and allocate healthcare efforts to patients that most benefit from early treatment. Methods: This study consists of a retrospective cohort of 158 patients, 57 of which had a DCIS recurrence over 2 to 228 months after treatment, and 45 progressed to invasive disease over 12 to 180 months. We generated a digital pathology library consisting of hematoxylin and eosin (H&amp;E) stained whole slide images (WSI), and applied machine learning methods to perform duct and nuclear segmentation followed by cell classification. We then computed a set of spatial statistics from landscape ecology to characterize each sample. We used LASSO on half of the data (the training set) to select a Cox proportional hazards model that predicted recurrence of DCIS or cancer and used the other half of the data (the test set) to validate the predictors in the Cox model. Results: When considering the time to recurrence as disease-free survival (DFS), the type of DCIS treatment stratifies patients between Lumpectomy Only and More than Lumpectomy groups (log-rank test p-value &lt; 0.0001), recapitulating previous results. When the subset of patients that had more than a lumpectomy was analyzed using spatial landscape ecology metrics, a decrease in the interaction between tumor cells and fibroblasts and between tumor cells and lymphocytes, measured by the mean nearest neighbor distance, were associated with improved DFS (concordance = 0.717) in the model validation set. Stratification of samples by the median risk is discriminatory with a p-value &lt; 0.001. Alternatively, decreased nearest neighbor distance between lymphocytes and fibroblasts was associated with the time to invasive disease (p-value &lt; 0.05). Conclusions: Integrating spatial ecological statistics of cell distributions enhances risk stratification of DCIS samples beyond the known effects of early treatment. Our results highlight the importance of ecological mechanisms in recurrence, progression and treatment resistance. The risk associated with spatial interactions of lymphocytes and fibroblasts suggests previously unknown ecological interactions may be associated with early micro-metastases that result in breast cancer even after mastectomy or lumpectomy with radiation in patients with DCIS.
United States treatment pattern response to carboplatin and cisplatin shortages.
Authors: John Kent Lin, Ying Xu, Mariana Chavez Mac Gregor, Jenny Jing Xiang, Changchuan Jiang, Ya-Chen Tina Shih
Journal: Journal of Clinical Oncology (2025)

11088 Background: In response to cisplatin (2/10/23) and carboplatin (4/28/23) shortages, national guidelines recommended prioritizing cisplatin and carboplatin for their most important indications to preserve availability while maximizing survival outcomes. We examined the real-world response to recent platinum shortages in lung cancers (LC), for which guidelines recommended preserving platinum chemotherapies when non-metastatic versus allowing substitution to immunotherapy monotherapy when metastatic. Methods: Using IQVIA PharMetrics data (9/1/22-10/31/23), we developed claims algorithms to identify intravenous anticancer therapy infusion claims for patients with incident non-metastatic vs. metastatic LC, which we validated against SEER-Medicare. Our outcomes were the fraction of infusions containing either (1) cisplatin, (2) carboplatin, or (3) immunotherapy (IO) without platinum. We performed an interrupted time series (ITS) analysis, using a linear probability model with three time-periods: pre-platinum shortage (9/1/22-1/31/23), post-cisplatin shortage (2/1/23-4/30/23), and post-carboplatin shortage (5/1/23-10/31/23), adjusting for age, gender, region, and comorbidities. Results: There were 11,983 infusions (2,411 patients) and 1,650 infusions (428 patients) for incident non-metastatic and metastatic LC, respectively. For non-metastatic LC, between 2/2023 (cisplatin shortage) and 04/2023 (carboplatin shortage), cisplatin use declined by 1.7 percentage point (pp) per month (p=0.03) and a non-significant 1.6 pp monthly increase in carboplatin usage (p=0.24). After carboplatin shortage, there was an immediate 4.5 pp drop in use of carboplatin (p=0.04), as well as an immediate 5.8 pp increase in use of cisplatin (p&lt;0.0001). Overall platinum use (receipt of either cisplatin or carboplatin) throughout this entire period did not have any significant change. For metastatic LC, cisplatin was rarely used, so results after the cisplatin shortage were not assessed. After carboplatin shortage, there was a 15.7 pp immediate decline in use of carboplatin (p=0.002), with a concomitant 18.4 pp immediate increase in use of IO without platinum (p&lt;0.001). Conclusions: The real-world response to platinum shortages in LC appeared to follow guidelines. For non-metastatic LC, for which platinum chemotherapy increases cure rates, declines during either the cisplatin or carboplatin shortages were equally balanced with increases in the other platinum chemotherapy, such that overall platinum chemotherapy usage remain stable. For metastatic LC, declines in carboplatin were balanced with increases in immunotherapy monotherapy.
Clinical features and survival outcomes of small, node negative breast cancer: A multicenter, retrospective study.
Authors: Linlin Sun, Xiaopei Dong, Hua Yang, Yingjian Sha, Yongsheng Jia, Yehui Shi
Journal: Journal of Clinical Oncology (2025)

e12554 Background: Early-stage breast cancer (T1N0M0 BC) generally has a favorable prognosis, but relapse risks persist over time. The role of adjuvant systemic treatments (AST) for tumors ≤10 mm remains debated. Given the limited availability of robust prospective data, retrospective studies play a crucial role in guiding clinical decision-making. Methods: This multicenter retrospective study analyzed 1,733 pathologically confirmed invasive T1N0M0 breast cancer patients treated at two Chinese medical centers between 1998 and 2018. Patients who received neoadjuvant chemotherapy or with unknown estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (HER2) status were excluded. The primary endpoint was disease-free survival (DFS). Secondary endpoints included distant recurrence-free survival (DRFS), breast cancer-specific survival (BCSS), and overall survival (OS). Clinicopathologic features and survival outcomes were assessed using Kaplan–Meier methods, along with univariate, and multivariate Cox regression models across four molecular subtypes defined by ER, PR and HER2 status. Tumor size thresholds for AST effectiveness were identified using maximally selected rank statistics. Results: Among the 1,733 T1N0M0 cases, HR+/HER2- was the most prevalent subtype (56.9%), followed by HR+/HER2+ (12.6%), HR-/HER2+ (10.8%), and triple-negative (19.7%). Molecular subtypes were significantly associated with survival outcomes. HR+/HER2- subtype had the most favorable 10-year DFS rate of 84.0%, while HR-/HER2+ tumors had the lowest (73.4%). Clinical features such as younger age, premenopausal status, and high histological grade were related to worse outcomes. In the multivariate analysis, early-stage patients treated with adjuvant chemotherapy showed significantly improved DFS compared to patients without AST with a hazard ratio of 0.33 (95% CI: 0.24 to 0.45, p &lt; 0.001). The benefit of AST was also identified for DRFS, BCSS and OS. Tumor size thresholds for AST benefit varied across four subtypes. For HR+/HER2- tumors, neither adjuvant chemotherapy nor endocrine therapy had benefit for tumors &lt; 10 mm ( p = and 0.33, respectively). In contrast, adjuvant chemotherapy with trastuzumab significantly improved DFS for HER2+ tumors ≥ 8 mm (HR = 0.37; 95% CI: 0.14 to 0.77, p = 0.018). Triple-negative tumors ≥ 9 mm also had improved DFS with AST (HR = 0.382; 95% CI: 0.21 to 0.74, p = 0.002). Conclusions: Overall, patients with early-stage breast cancer have a good prognosis. Subtype-specific tumor size thresholds offer a promising approach for decision making of AST in T1N0M0 breast cancer. Patients with larger tumor size might benefit from AST, warranting further investigation in prospective studies.
Effect of obesity on the survival outcomes among Indian women with breast cancer.
Authors: Hari Krishna Raju Sagiraju, Ajay Gogia, Aastha Goel, Atul Batra, Dayanand Sharma, Surendra Kumar Saini, Ashutosh Mishra, S.V.S. Deo, Chandra Prakash Prasad, Pranay Tanwar, Sandeep Mathur
Journal: Journal of Clinical Oncology (2025)

e12558 Background: Studies have reported increased risk of breast cancer (BC) with obesity in Indian population and few studies also highlighted the association of Body Mass Index (BMI) with BC types and chemotherapy response. However, no study till date has reported the effect of obesity on the survival outcomes among Indian women with BC. Methods: We retrospectively analyzed the medical records of women registered at our institute with a diagnosis of BC between years 2013-2024. Along with demographic, clinical and tumor characteristics, baseline weight and height were retrieved from the medical records. Primary outcomes were disease-free survival (DFS) and overall survivals (OS). Cox-regression and Kaplan-Meier survival analysis were used to examine the association of BMI with survival outcomes. Results: Study sample consisted of 1303 women with BC. Median age of the study cohort is 46years and 42% were postmenopausal. Fifty-one percent of the cases were Hormone receptor (HR) positive and Her2Neu negative, followed by 29.4% (HR+ and Her2Neu+), 14.5% (TNBC) and 5.3% (HR- and Her2Neu+). AJCC-Stage I &amp; II tumors consisted of 42.6%, followed by 41.1% Stage-III and 16.3% Stage IV tumors. Forty-one percent were obese (BMI&gt;27.5kg/m 2 ) and 35% were overweight (BMI: 23-27.5kg/m 2 ). Fifty percent of postmenopausal women were obese at time of presentation compared to 34.5% of premenopausal women (p&lt;0.001). Proportions of those with ECOG score ≥2 and with one or more comorbidity were significantly high among obese group compared to other BMI categories (p&lt;0.05). Family history of cancer, clinical stage, HR/Her2Neu receptor status and type of surgery received did not vary across the BMI categories. Median DFS for overweight (110 months) and obese (107.2 months) individuals was significantly lower compared to normal/underweight individuals (194 months). After adjusting for menopausal status, comorbidity, clinical stage and receptor status; BMI≥27.5 was significantly associated with poor DFS [aHR(95%CI):1.78(1.25-2.55), p=0.002]. Premenopausal status, Stage&gt;II and ECOG score≥2 were associated with poor DFS. Among those with non-metastatic breast cancer at time of presentation, BMI≥27.5 was associated with poor overall survival [aHR(95%CI):1.91(1.01-3.63), p=0.047], however, BMI was not associated with overall survival among those who with metastatic breast cancer. The effect of BMI on the survival outcomes didn’t vary by menopausal status and receptor status. Conclusions: BMI is independent predictor of survival among Indian women with BC. Clinical trials with multimodal therapeutic approaches including nutritional and lifestyle interventions are required to improve the survival outcomes among obese women with BC.
A quality improvement initiative to improve fertility counseling and referral procedures for cancer patients at a university cancer center.
Authors: Phyo Thazin Myint, Monika Kulasekaran, Mohammed Saleh, Matthew Stein, Kushal Naha
Journal: Journal of Clinical Oncology (2025)

e23210 Background: Fertility counseling is crucial in cancer care as treatments can impair fertility, causing emotional distress and reducing quality of life. Early discussions about infertility risks and referrals to reproductive specialists are vital. Despite guidelines like ASCO 2018, fertility counseling and referral rates remain low, 43.5% and 4-30%, respectively. This quality improvement (QI) project aims to enhance fertility counseling and referrals by addressing barriers to these processes. Methods: We conducted a retrospective chart review of cancer patients (ages 18-45) newly diagnosed or with recurrence requiring systemic therapy at Ellis Fischel Cancer Center between 1/1/2020 and 8/31/2024. We also distributed anonymous surveys to patients [ages 18-45, newly diagnosed with cancer between 2/1/2024 and 7/31/2024] and providers [attendings, fellows, and advanced practice practitioners] at the cancer center. Results: The chart review found that 43.5% (40 of 92) of patients had documented discussions on infertility risks before systemic therapy with only 18.5% receiving referrals for fertility counseling. The age distribution was similar between patients who did or did not receive counseling. 66.7% of surveyed patients reported that their provider discussed infertility risks, suggesting under-documentation of these discussions. Despite a low documented counseling rate, 89% of patients reported feeling confident in their fertility preservation decisions. However, only 22% of patients answered all fertility knowledge questions correctly, indicating a potential gap in awareness of available options. These findings highlighted infertility risk discussion and fertility counseling documentation as potential areas of improvement. In response, we created a patient education pamphlet and a Smart Phrase to improve documentation. Of the surveyed providers, 58.8% were female, with experience ranging from 1 to 16 years. While 58.8% of providers felt comfortable discussing fertility preservation, only 29.4% believed their knowledge was sufficient for effective counseling. Most providers (88.2%) agreed that written educational materials would be beneficial. Key barriers identified included the urgency of cancer treatment, limited access to fertility preservation services, and the lack of an efficient referral system. In response, we compiled a list of local fertility preservation clinics with urgent referral guidelines and developed provider education resources. We also plan to advocate for this initiative at oncology meetings and grand rounds within the institution. Conclusions: Our baseline data above highlighted key quality metrics and informed the development of targeted interventions. These interventions are currently being implemented. We plan to collect post-intervention data after 6 months to assess their impact.