bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantification of treatment-related time toxicity in patients with advanced stage cancer.
Authors: Zhou Yuhang , Fitzpatrick Madeline Campbell , Hurtado Castillo Marisabel , Sessions William Steele , Aung Kyaw Lwin , Pandey Om Narayan , Goodgame Boone Wilder 
Published: June 2024

12137 Background: Treatment-related Time Toxicity (TrTT) in cancer patients is a recently proposed metric to describe the burden of time spent in pursuing medical care, including office/hospital visits, side effect management, lab tests, imaging scans, and travel time. Time toxicity is a valuable concept in patient-centered shared-decision making, especially in the palliative management of end-stage cancer patients with limited life expectancy. Despite tremendous progress in cancer treatment, most of the guideline-recommended treatment options convey short survival benefits of around 3-6 months when compared to supportive care. While medication toxicities are meticulously reviewed with patients, the cumulative burden and impact of time are rarely included in the discussion, partly due to the lack of standardized measurement of TrTT, and the absence of data from clinical trials. Thus, there is a compelling need for the quantification of time toxicity to guide clinical practice and patient preference. Methods: This was an observational analysis of the time toxicity from palliative treatment for patients with incurable solid tumors at a regional safety-net oncology office that focuses on underserved communities. Time toxicity is calculated as the number of days a patient spent with any healthcare-related encounters during a 3-month period from our medical record system. Results: The median age of the total 94 included patients was 57. 54% of the population were Hispanic. The ratios for mild, moderate, and severe time toxicity were 38%, 44%, and 18%, respectively. Immunotherapy was associated with significantly less time toxicity (TrTT = 8.5 days, 95% CI = 6.3-9.7) compared to chemotherapy (26.3 days, 95% CI = 18.3-34.4, P < 0.001), while targeted therapy (13.4, 95% CI = 7.0-19.8, P = 0.01) and hormone therapy (11.9, 95% CI = 4.1-19.7, P < 0.01) also has lower TrTT than chemotherapy. Patients with a worse performance status (ECOG PS 3-4, 30.1 days, 95% CI = 18.3-41.9) experienced higher time toxicity than fitter patients (PS 0-2, 16.7 days, 95% CI = 13.2-20.2, P < 0.05). Among different cancer types, gastrointestinal malignancies were associated with the highest time toxicity with average TrTT of 30.2 days/3 months (95% CI =22.5-37.2). Conclusions: This is the first comprehensive study to quantify real-world Treatment-Related Time Toxicity across multiple cancer and therapy types in an underserved population. We found 18% of patients with incurable solid malignancies experienced severe time toxicity (> 1 in 3 days). Higher TrTT was associated with cytotoxic chemotherapies, GI malignancies, and poor performance status. Time toxicity should be taken into account with other quality-of-life outcomes in treatment pathways and patient-centered oncology care.
Risk assessment of myelosuppression with PARP inhibitors in prostate cancer: Insights from FAERS.
Authors: Jatwani Karan , Sarangdhar Mayur , Kolli Venkatesh , Khosla Atulya Aman , Gopalakrishnan Dharmesh 
Published: June 2024

12144 Background: DNA damage repair defects are prevalent in 15% of Prostate cancer. FDA has approved four PARP inhibitors to be used for metastatic castration resistant prostate cancer (mCRPC). Based on the approvals, currently Olaparib, Niraparib, Rucaparib and Talazoparib have been approved alone or in combination with an Androgen receptor pathway inhibitor (ARPI). All these agents have myelosuppressive (MS) events . We aimed to assess the pharmacovigilance (PV), reporting rate, and reaction outcomes of different approved PARP inhibitors in the mCRPC setting reported to the United States Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: We analyzed MS events in 2,137 patients on PARP inhibitors. MS event reports were submitted to FAERS between 2004-2023 and analyzed using the AERS Mine framework. MS incidences were analyzed within different PARP inhibitors utilized for prostate cancer. Our primary composite endpoint was the PV of MS caused by olaparib, rucaparib, niraparib and talazoparib for the treatment of mCRPC. PARP inhibitors were analyzed as mutually exclusive cohorts. Standard PV metrics were used to determine MS association with PARP inhibitors and two-way ANOVA testing was used to evaluate for statistical significance across the treatment groups. Results: 2,137 prostate cancer patients on PARP inhibitors were identified. MS events (n=677)– olaparib (227/831, 27.32%), rucaparib (n = 171/495, 34.55%), niraparib (63/111, 56.76%), and talazoparib (34/47, 72.34%). MS events were further categorized into five classes – anemia (n = 311, 45.94%), leukopenia (n = 123, 18.17%), thrombocytopenia (n = 107, 15.81%), pancytopenia (n = 102, 15.07%), and myelodysplastic syndrome (MDS) or Acute Myeloid Leukemia (AML, 34, 5.02%). Anemia rates across the four cohorts were identical (table). Leukopenia rates were highest in talazoparib cohort (27.66%). Thrombocytopenia rates were highest in niraparib cohort (24.32%). For pancytopenia, rates were highest in talazoparib 10.64%. MDS/AML rates were Talazoparib (2.13%), Olaparib (1.8%). The relative rates of MS events were significantly different across the PARP inhibitors (p < 0.05, Table). Conclusions: This is the first study specific to prostate cancer analyzing the relative rates of MS events across different PARP inhibitors. Talazoparib exhibited the highest incidence of myelosuppression. This underscores the careful patient selection and vigilant monitoring needed in PARP inhibitors. Prompt recognition of myelosuppression is needed to optimize the safety and efficacy of PARP inhibitor therapies in prostate cancer. [Table: see text]
Ensuring precision medicine for veterans with lung cancer: A randomized clinical trial.
Authors: Thomas Mikalah , Gonzalez Hector , Agrawal Madhuri , Das Millie , Patel Manali I. 
Published: June 2024

1501 Background: National guidelines recommend molecular testing for patients with non-small cell lung cancer, yet this is not routinely completed in practice. In prior work, we identified barriers to testing in the Veterans Affairs. First, physicians are unaware if and when testing has occurred. Second, patients are unaware of testing and the implications on treatment and clinical outcomes. In response, in collaboration with a Veteran and Caregiver Advisory Board, we created a multilevel intervention led by a peer volunteer who: 1) provides tailored education of molecular testing for 1 month for Veterans newly diagnosed with lung cancer and, 2) alerts clinicians via secure messaging if tumor samples have not been tested. We conducted this randomized clinical trial to assess whether the intervention improves patient knowledge of molecular testing and patient activation and increases receipt of molecular genomic testing compared with usual care. Methods: All veterans >18 years old with a new diagnosis of any stage non-small cell lung cancer were eligible. Exclusions included inability to consent. All participants completed baseline assessments at randomization and at 1-month follow-up comprised of a 3-question validated precision medicine knowledge questionnaire and a 13-question validated patient activation measure. Genomic testing was assessed by chart review at 6-months followup. We used regression models to assess differences in mean knowledge and activation scores over time between groups and compared rates of genomic testing. Results: 75 Veterans were screened with 46 eligible. All 46 consented to participate, with 23 in each group. All were male (100%), had mean age of 76.3+/-5.54 years; 31 (67.4%) were Latinx; 2 (4.4%) Asian; 11 (23.9%) Black; 1 (2.2%) Native Hawaiian; 32 (69.6%) White; 32 (69.6%) had Stage 4 disease. At 1 month follow-up mean knowledge scores increased over time in the intervention group and remained stable in the control group (mean score 2.30 +/- 0.97 versus 1.16 +/- 1.15, p=0.014). At 1-month follow-up, patient activation increased for the intervention group more than the control group (46.7 +/- 11.3 versus 37.6 +/- 9.26, p=0.002). There were no significant differences in tumor testing between groups (intervention: 95.6% versus control: 86.9%, p=0.62). Conclusions: A volunteer led effort improved patient education regarding precision medicine and patient activation among Veterans as compared with usual care alone. Interventions that include volunteers and other support can enhance care and clinical outcomes among veterans with lung cancer. Clinical trial information: NCT05795959 .
Patterns of screening and access to care in women diagnosed with breast cancer in a safety-net system.
Authors: Kanjwal Shifa , Su Malcolm , Hopson Madeleine , Kashanian Sarah , Ahn Chul , Syed Samira K. , Delgado Ramos Glenda Maria , Unni Nisha , Sadeghi Navid 
Published: June 2024

1531 Background: Screening mammography reduces breast cancer specific mortality by allowing diagnosis at an earlier stage. Inconsistent guidelines and limitations in access to primary care are among factors that affect screening uptake. In this study, we reviewed patient characteristics, access to care, and uptake of screening mammogram (SMMG) prior to a breast cancer diagnosis at a safety-net system. Methods: Parkland Health (PH) is the safety-net system for Dallas County and is affiliated with the University of Texas Southwestern Simmons Comprehensive Cancer Center. Electronic medical records of patients with a new invasive breast cancer diagnosis between 2018 and 2019 at PH were reviewed and data on demographics and clinical presentation were collected. Patients were categorized based on SMMG uptake prior to their cancer diagnosis: never-screened, ever-screened (SMMG > 2 years prior to diagnosis) and recent-screened (SMMG within 2 years of diagnosis). Results: A total of 468 new breast cancer cases were identified (48.7% Hispanic; 32.9% Black). Of these, 12.2% were younger than 40 and 27.8% were 40-49. Payer mix included 64.7% uninsured and 34.8% Medicare/Medicaid. Half of the patients (50.6%) never had SMMG. Never-screened rate was higher among younger patients (98.2% in < 40; 53.8% in 40-49; and 39.4% in >50; p < 0.0001). Only 25.2% (118/468) of the patients had a SMMG within 2 years prior to their diagnosis. Among patients aged >40, 44.0% (181/411) were never-screened and 28.7% (118/411) were recent-screened. Screen-detection rate among patients >40 was 40.1% (165/411), of whom 22 (13.3% of screen-detects) were diagnosed with cancer on their first ever SMMG. Race/ethnicity, insurance status, and positive family history of cancer were not significantly different between never, ever, and recent-screened patients. Screening uptake correlated with nodal status, tumor size and stage at diagnosis. Patients who had a prior encounter at Parkland were more likely to have a SMMG. Patients established with Parkland primary care at diagnosis were more likely to have early-stage disease. In patients < 40, 24.6% presented with stage IV disease at diagnosis and 57.9% did not have a primary care provider. A positive family history of breast/ovarian cancer or any cancer was noted in 38.6% and 57.9% of patients < 40, respectively. Conclusions: In the cohort presented here, 27.8% of patients were between age 40-49, reflecting the impact of a gap in the guideline, which is now addressed in the most recent iteration of USPSTF guidelines. Connecting ED users to primary care, identifying higher risk individuals through family history, and increasing SMMG rate in the primary care setting are among strategies to reduce the rate of late-stage diagnosis of breast cancer, particularly among vulnerable patient populations.
The influence of access to care on radiation therapy utilization among older women with breast cancer.
Authors: MeLampy Jane , Chen Mu-Hsun , Mazumdar Madhu , Klein Paula , Marcadis Andrea , Marti Jennifer L 
Published: June 2024

1539 Background: In 2004, the Cancer and Leukemia Group B (CALGB) C9343 trial compared outcomes among women aged ≥ 70 who had stage I, estrogen-receptor positive (ER+) breast cancer (BC) who received a lumpectomy and hormonal therapy, treated with and without radiation therapy (RT). While the trial found that overall survival was not different between groups, RT use did not significantly decline after its publication. We investigated regional variation in RT utilization in the United States (US) and its association to access to healthcare among women aged ≥ 70 diagnosed with localized ER+ BC after partial mastectomy (PM) between 2004–2020. We hypothesize that women with greater access to health care will more often receive RT. Methods: To examine regional variation in the use of RT and its association with access to healthcare, we extracted data from the Surveillance, Epidemiology, and End Results 17 registry. From 2004 – 2020, 105,694 women aged ≥ 70 years who received a PM for localized ER+ BC across 199 US counties with a population > 50,000 were identified. Access to healthcare was measured using six socioeconomic (SES) variables: median household income, % with high school education, % of families above poverty line, % with bachelor’s degree, % speaking English, and % employed, measured from 2015-2019. A multivariable linear regression model tested for association between each variable and RT use. A composite SES score was calculated as the sum of z-scores across all six variables and we identified the top 20 and bottom 20 scoring counties. SES score was used as a surrogate for access to healthcare and was correlated to RT use using a linear regression model. Results: The percentage of women aged ≥ 70 receiving RT after PM for localized ER+ BC varied across counties from 19.0% to 84.6%. In 2004, receipt of RT was similar between the top 20 and bottom 20 counties (67.8% vs 65.2%, respectively). This decreased after publication of CALGB C9343; by 2020 receipt of RT was 58.7% for the top 20 and 49.2% for the bottom 20 SES counties (Table). In linear regression analysis, SES was significantly associated with RT use (p<0.0001). In multivariable linear regression analysis, only % above high school education was significantly associated with RT use (p=0.0205). Conclusions: Among women aged ≥ 70 with localized ER+ BC who underwent PM and who were candidates for omission of RT, those who lived in higher SES counties were more likely to receive RT from 2004 – 2020. Since the CALGB C9343 trial in 2004, RT use has declined; however, the decline was less among those with higher SES and therefore likely less among those with greater access to healthcare. These women may experience over treatment, which may result in personal or system wide harms. [Table: see text]
Trend and burden of cancer related hospitalizations in the US: Insights from a National Inpatient Sample Database analysis.
Authors: Singh Rohit , Jatwani Karan , Chitkara Akshit , Batra Akshee , Batra Nitya , Rubens Muni , Ramamoorthy Venkataraghavan , Rabishanker Veda , Saxena Anshul , Khosla Atulya Aman 
Published: June 2024

1543 Background: Cancer-related hospitalizations constitute one of the primary drivers of cancer-related healthcare expenses. Understanding the burden and characteristics of these hospitalizations is essential to appropriately direct resources, improve outcomes, and avoid unnecessary hospitalizations. Our study sought to evaluate the national trends, reasons, disposition status, length of stay, and associated costs for cancer-related hospitalizations. Methods: We conducted a retrospective analysis of cancer hospitalizations using the 2008-2019 National Inpatient Sample. Cancer-related hospitalizations were identified using validated Clinical Classification Software (CCS) codes 11 to 45. Trends in hospitalization volume, mortality, length of stay, disposition status, and costs were analyzed overall and by cancer type, adjusted according to the US Consumer Price Index. Results: There were 371 million weighted hospitalizations from 2008 to 2019, of which 15.1% (56 million) were cancer related. The most reported cancer types were breast cancer (11.9%), secondary malignancies (11.2%), and prostate cancer (10.3%). The most common reasons for cancer-related hospitalizations were septicemia (4.8%), pneumonia (4.7%), and complications of surgical procedures or medical care (3.1%). Trend analysis showed that the total number of cancer-related hospitalizationsincreased from 12,963 to 16,500 per 100,000 hospitalizations during the study period (relative increase, 27.3%). Mortality rates decreased from 5.1% to 4.0% (relative decrease, 21.6%), while the length of stay decreased from 3.5 to 3.2 days (relative decrease, 8.6%) during the study period. Disposition to home or short-term facilities decreased (relative decrease, 3.1%), while to long-term facilities increased (relative increase, 20.6%) during the same period. Total hospitalization cost increased from $55.5 billion in 2008 to $76.4 billion in 2019 (a relative increase of 37.7%). Conclusions: Our analysis shows that while cancer-related hospitalizations and associated costs increased substantially, mortality rates, and length of stay decreased. These trends reflect rising cancer burden but improvements in hospital cancer care quality and efficiency. Ongoing efforts to optimize cancer treatment, minimize unnecessary hospitalizations, reduce length of stay, and facilitate safe discharges are needed to improve patient outcomes and control costs further. Continued surveillance and health policies should address the rising demand for high-value cancer care.[Table: see text]
Deep learning vs traditional breast cancer risk assessment models: Are we offering supplemental services to those who benefit the most?
Authors: Lamb Leslie R. , Albasha Heba , Mercaldo Sarah , Carney Andrew , Lehman Constance D 
Published: June 2024

1556 Background: Mammography-based deep learning (DL) risk stratification models improve discriminatory accuracy in predicting future breast cancer compared to traditional risk models; however, decisions for and insurance payment of supplemental services such as MRI are driven by traditional scores. The purpose of this study was to compare risk scores and cancer detection rates in patients identified as increased risk by DL vs traditional models in a large screening mammography cohort. Methods: This multisite study included consecutive patients ≥40y undergoing routine bilateral screening mammography from 9/2017 to 1/2022 at five facilities. Tyrer-Cuzick version 8 (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models and a DL 5-year model were used to assess risk. The following thresholds were used to define intermediate risk: ≥1.67% for TC and BCRAT 5-year, 2.2 for DL model and high risk: ≥20% for TC and BCRAT lifetime, ≥6.0 for DL model. Patients were included if all risk scores were available. Patient demographics were retrieved from electronic medical records and cancer outcomes through regional tumor registry linkage. The proportion of increased-risk patients and cancer detection rates (CDR [cancers per 1000 women screened]) across models were compared using Pearson’s Chi-square test. Results: 148476 exams in 69464 patients (mean age 58.0y [IQR: 50.0-67.0]) were performed. 81.7% (119386/146075) of exams were in White patients and 18.3% (26689/146075) in races other than White. CDR of those classified as intermediate risk by DL was 7.1 (450/63109) vs 4.7 (320/68545; P<0.001) by TC and 4.2 (300/70831; P<0.001) by BCRAT, and as high risk was 20.6 (305/14772) by DL vs 3.9 (64/16442; P<0.001) by TC and 5.0 (29/5782; P<0.001) by BCRAT. 13.0% (19248/148476) were classified as intermediate risk by DL but not by TC or BCRAT, with a CDR of 6.0 in this cohort. 32.0% (47435/148476) were classified as intermediate risk by TC or BCRAT but not by DL with a CDR of 1.1. 8.5% (12577/148476) were classified as high risk by DL but not by TC or BCRAT with a CDR of 20.8. 11.1% (16451/148476) were classified as high risk by TC or BCRAT but not by DL with a CDR of 1.9. Conclusions: A significant proportion of patients identified as increased risk by DL model are not assessed as increased risk by commonly used risk models, effectively excluding them from accessing potentially life-saving supplemental services. DL scores met the American College of Radiology's acceptable CDR on screening mammography (≥2.5) regardless of traditional scores, however, elevated traditional scores in the absence of elevated DL scores did not meet acceptable standards. Risk assessment guidelines incorporating DL models are necessary to capture patients who benefit the most.
Adaptively learning visual model of surgical resection images via coarse-to-fine–grained strategy for pathological invasiveness prediction in indeterminate stage IA lung adenocarcinoma.
Authors: Yao Lintong , Zhou HaiYu , Cai Linghan , Li Qiaxuan , Xie Daipeng , Lin Hongwei 
Published: June 2024

1563 Background: Indeterminate nodules with high risk of malignancy are suggested to receive video-assisted thoracoscopy biopsy. Frozen section assists surgeons to decide surgical strategies for stage IA lung adenocarcinoma (LUAD) intraoperatively, which is time-consuming and may misdiagnosis due to limited sampling and poor tissue quality. Methods: We prospectively enrolled stage IA LUAD patients underwent complete resection between June 2020 and September 2023 in Guangdong Provincial People's Hospital (GDPH), Affiliated Hospital of Guangdong Medical University, Meizhou People's Hospital. All the resected lung nodules were finally diagnosed as pre-invasive lesion (PIL), minimally invasive adenocarcinoma (MIA) or invasive lung adenocarcinoma (IAC) by FFPE pathological diagnosis. Images of the whole section of the lung nodules were taken by smartphones under natural lighting condition in operating theaters without shadowless surgical lights. Patients received preoperative anti-cancer therapy and with low-quality tumor section images were excluded. Images of nodules section, FS reports, FFPE diagnosis and clinical information were prospectively collected. Predictive artificial intelligence (AI) model was developed by a three-step process, which first predict the IAC and non-IAC region at coarse level, and identify fine level features through the high-risk region. The coarse and fine labels were then coordinated through risk ranking loss to reach a final predictive result. Results: We prospectively enrolled 1516 patients with 1638 indetermined lung nodules from preoperative chest CT following the inclusion and exclusion criteria, with 2438 images acquired from intraoperative nodule sections. The included patients have median age of 57 years old and 38.6% male patients. The pathological diagnosis for included lung nodules are 122 PIL nodules, 502 MIA nodules and 1014 stage IA LUAD. A multi-task artificial intelligence based on Coarse-to-Fine-Grained Strategy has been established to classify the indetermined lung nodules during the surgery. For binary classification, AI model reach an AUC of 0.86 in differentiation between IAC or non-IAC. For 3-classification level, AI model reach an AUC of 0.87 in differentiation between PIL, MIA or IAC. We further defined the IAC nodules into G1, G2 and G3 according to IASLC lung adenocarcinoma grading system. For 5-classification level, AI model reach an AUC of 0.86 in differentiation between PIL, MIA, IAC-G1, IAC-G2 and IAC-G3. Conclusions: Our AI models based on images of intraoperative surgical resection could effectively classify indetermined lung nodules, which can assist thoracic surgeons diagnose the nodules rapidly during the surging and decide the following surgical strategies. Clinical trial information: ChiCTR2300075999 .