bluuebunny/crossref_metadata_2025_split huggingface dataset:
Utility of axillary surgery in patients with de novo metastatic breast cancer undergoing surgery of the primary site.
Authors: Vasigh Mahtab , Ruth Karen J , Bleicher Richard J. , Aggon Allison A. , Pronovost Mary , Porpiglia Andrea S , Williams Austin D. 
Published: June 2024

1086 Background: Approximately 6% of patients have de novo metastatic breast cancer (MBC) for whom surgery is reserved for special circumstances since it confers no survival benefit. The prospective studies evaluating surgery of the primary site in MBC recommended standard surgical management of the axilla. However, it is unclear whether axillary surgery is of any utility among these patients. We sought to evaluate the survival benefit of axillary surgery in MBC patients undergoing primary site surgery. Methods: From the National Cancer Database (2012-2020), we identified patients aged 18-79 with MBC undergoing surgery of the primary site and grouped patients according to axillary surgery performed: none, sentinel lymphadenectomy (SLNB), or axillary lymph node dissection (ALND) with or without SLNB. We performed a landmark analysis beginning 12 months after diagnosis to assess overall survival (OS) and used Cox regression to adjust for clinicodemographic characteristics. Results: We identified 7,808 patients, 20.2% of whom had no axillary surgery, 16.3% had SLNB, and 63.5% had ALND. Of the patients who had axillary surgery, 26.3% were pN0. Patients who had no axillary surgery, were older (mean 57.2 vs 60.3 years), more likely to have tumor histology other than ductal or lobular (8% vs. 4%), be cN0 (28% vs. 20%), less likely to have bone-only metastases (36% vs. 44%) and undergo breast-conserving surgery (49% vs. 24%) [all p<0.001]. The mean and median number of positive nodes was 1.3±2.3 in the SLNB, and 5.9±6.8 in the ALND group. 36-month adjusted survival estimates were 71.4%, 66.5%, and 58.0% for SLNB, ALND, and no axillary surgery groups, respectively. On univariate analysis and after adjusting for clinicodemographic features, both SLNB and ALND remained independently associated with improved OS when compared with no surgery (Table). Conclusions: As in prior retrospective analyses of surgery for the primary site, we found that the performance of axillary surgery in MBC patients was associated with improved OS. Selection bias is likely a contributing factor as surgeons are more likely to recommend axillary surgery for patients with a better functional status. Of concern is the number of MBC patients being exposed to the morbidity of ALND who are ultimately pN0, and thus derived no benefit. These findings highlight the need to carefully weigh the risks and benefits before performing axillary surgery. Prospective studies evaluating axillary surgery are needed to validate these results. [Table: see text]
SV-BR-1-GM after progression on ADC in patients with metastatic breast cancer.
Authors: Nangia Chaitali Singh , Calfa Carmen Julia , Bayer Blaise , Chang Mingjin , Aghajanian Tamar , Williams William , Del Priore Giuseppe , Wiseman Charles L. , Chumsri Saranya 
Published: June 2024

1087 Background: Antibody-drug conjugate (ADC) sequencing raises concerns of cross-resistance due to their shared targets and cytotoxic payloads. Consequently, treatments after ADCs in heavily pretreated MBC is uncertain. SV-BR-1-GM, a GM-CSF secreting, antigen-presenting immortalized breast cancer cell line, has emerged as a post ADC solution (1) based on a different MOA. We now report the results of SV-BR-1-GM in ADC treated patients. Methods: Post-hoc analysis of an ongoing randomized Phase 2 censored to the data-cut date prior to data lock. Bria-IMT (~20x106 SV-BR-1-GM cells, intradermally 48-72 hours after ctx (300 mg/m2), followed by interferon-alpha 2 days later) administered q3wks with a checkpoint inhibitor (CPI). Adverse events were classified according to their severity and relationship to treatment regimen. Results: Among 54 advanced MBC patients randomized, 23 who failed prior ADC therapy were identified. All were heavily pretreated (median 6 prior lines, range 3 – 13). Median age 62 (41-83). 14 pts had HR+/HER2-, 5 TNBC, and 2 HR+/HER2+ MBC. The remaining were HR-/HER2+ or HR-/HER2 low. 8 pts received > 1 ADC. Prior ADCs included: 4 ado-trastuzumab emtansine, 14 trastuzumab deruxtecan, 13 sacituzumab govitecan. 7 pts had also received ≥ 1 prior CPI line. To date, pts received a median 3 cycles of Bria-IMT™ + CPI (2-8). Kaplan-Meier analysis revealed a median OS of 42 weeks with survival of 69% at 6 months. The subset experienced a median PFS of 74 days with 40% of pts demonstrating a Bria-IMT PFS (PFS2) similar to or > their prior line PFS (PFS1). 39% of pts experienced a PFS2 ≥ their most recent line of ADC PFS. The disease control rate was 40% among evaluable pts. 43% of pts had a reduction in cancer-associated macrophage-like cell (CAML) levels; median reduction was 36% (22-98%) after 1 cycle. There were no toxicity-related treatment discontinuations. 43% (10/23) reported grade 3 or 4 AEs, independent of study drug causality. AEs included injection site reaction (39%), fatigue (26%), and nausea/vomiting (43%), mostly mild to moderate. One case of elevated lipase was the most clinically significant grade 4 AE. No interstitial lung disease (ILD) was reported. Conclusions: This subset analysis of the Bria-IMT regimen in ADC refractory MBC patients suggests a potential clinical benefit and treatment option for this patient population. The absence of serious AEs, notably interstitial lung disease (ILD), and no toxicity-related treatment discontinuations, underscores the regimen's favorable safety profile. Future studies are warranted to confirm these results and explore the potential of Bria-IMT™ in broad clinical settings of heavily pretreated contemporary MBC patients. 1. Kamaraju 2024 AACR. Clinical trial information: NCT03328026 .
Racial disparities among triple negative breast cancer clinical trial enrollees between 2010-2023.
Authors: Rao Divya , Brabant Paige , Chau Isabelle , Darrell Megan , Yao Chi-Yen , Roth Melanie , Pandit Maya , Gary Monique , Etienne Mill 
Published: June 2024

1093 Background: Despite the social construct of race lacking biological significance, it plays a significant role in health disparities and necessitates examination to address social stratification and health inequities. Not reporting race in medical research neglects the reality of social stratification, injustices, and implications for population health. Additionally, excluding racial demographic data from research publications may conceal health disparities. This study focuses on Triple Negative Breast Cancer (TNBC) clinical trials, aiming to meta-analyze the inclusion of racial data and assess racial representation. Methods: A meta-analysis of 81 completed TNBC trials (2011-2023) from ClinicalTrials.gov was conducted. Demographic data on race and sex were extracted and analyzed using Microsoft Excel. Two independent reviewers extracted study-level data for a random-effects analysis. The primary outcome was identifying the prevalence of studies lacking racial data. The secondary outcomes are the prevalence of each racial category (White, Black, Asian, Hispanic/Latino, and Combined Other) among all TNBC clinical trials that reported demographic information. The Combined Other category includes American Indian, Native Hawaiian, more than one race, and unknown/unreported. Results: Of 81 trials, 29.63% did not report racial data. The remaining 57 trials (8,196 participants) revealed 73.98% White, 10.29% Asian, 7.85% Black, 8.02% Hispanic/Latino, and 4.12% "combined other" participants. U.S.-based trials (36 studies, 3,348 participants) showed 77.96% White, 12.03% Black, 4.51% Asian, and 4.96% Hispanic/Latino participants. Moreover, we conducted a comparative analysis between the racial distribution in U.S. clinical trials and the prevalence of TNBC in the U.S. population, utilizing the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Public Use Datasets (2010-2019). The analysis revealed a significant discrepancy with White (p<.001) participants being overrepresented, and Black (p<.001), Asian (p<.001), Hispanic/Latino (p<.001), American Indian (p<.001), more than one race (p<.02), and Other/Unknown (p<.001) populations being underrepresented. Conclusions: Globally, 29.63% of TNBC trials did not report racial data, raising concerns about generalizability. U.S.-based studies exhibited overrepresentation of White participants and underrepresentation of Black, Asian, Hispanic/Latino, American Indian, Native Hawaiian, and other categories. Notably, Black and Hispanic/Latino individuals, comprising a substantial portion of TNBC patients, were underrepresented. These findings underscore racial/ethnic disparities in breast cancer trial participation, warranting further investigation into recruitment barriers for equitable participation.
Current management practices of de novo oligometastatic breast cancer: Real-world data from a physician survey.
Authors: Odzer Nicole , Pusztai Lajos , Rozenblit Mariya 
Published: June 2024

1104 Background: De novo oligometastatic breast cancer (oligo-mBC) is generally defined as < 5 lesions at the time of diagnosis and represent a steadily increasing subset of metastatic cancers. Several retrospective studies demonstrated prolonged overall survival (OS) for select patients with oligo-mBC when treated with multimodality therapy (chemotherapy, surgery, and radiation). However, randomized trials that tested surgical resection of the primary tumor, or radiation of oligometastases (including recurrent and de novo disease) showed no OS benefit. No randomized trials to date have tested all 3 treatment modalities combined (as used with curative intent in early-stage disease). Current NCCN guidelines do not include specific recommendations for de novo oligo-mBC, the St Gallen International guidelines allow multimodality therapy for carefully selected patients. We surveyed practicing medical oncologists through the ASCO survey pool regarding their use of multimodality therapy in de novo oligo-mBC. Methods: An online survey was sent to the ASCO Research survey pool (N=999) between 11/14/23 – 12/29/23, 193 ASCO members completed the survey. Data was confidential and anonymous. Results: The majority (76-79%) of respondents recommend starting with palliative systemic chemotherapy. If patients have a response to initial chemotherapy, 42-54% recommend ablative radiation to all residual lesions, and 38-52% recommended surgical resection of the primary tumor. Overall, multimodality therapy is recommended sometimes by 29-36%, often by 19-29%, almost never by 32-41%, and never by 2-7% of respondents. Recommendations varied by disease subtype (Table). Conclusions: These survey data indicate varied practices in the treatment of de novo oligo-mBC. This highlights the need for future randomized clinical trials to investigate whether there may be survival benefit by receptor subtype in de novo oligo-mBC treated with curative intent multimodality therapy as used in locally advanced cancers. [Table: see text]
Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC).
Authors: Yadav Monica , Lee Jeeyeon , Kim Haseok , Lee Seyoung , Um Taegyu , Lee Salie , Djunadi Trie Arni , Chung Liam IL Young , Yu Jisang , Rodrigues Darren , Gennaro Nicolo , Kim Leeseul , Kim Yuchan , Nam Myungwoo , Hong Ilene , Jang Jessica , Cho Amy , Kang Grace , Velichko Yury , Chae Young Kwang 
Published: June 2024

12142 Background: The increasing use of immunotherapy in advanced non-small cell lung cancer (NSCLC) presents a significant challenge in managing adverse events, particularly checkpoint inhibitor-associated pneumonitis (CIP). This potentially life-threatening complication often necessitates discontinuation of immunotherapy, even in patients experiencing tumor response. Presently, there are no reliable models to predict the onset of CIP. This research utilizes CT radiomics to create an innovative approach for anticipating the risk of CIP in patients with NSCLC. Methods: This IRB-approved, retrospective study analyzed data from 159 stage III-IV NSCLC patients undergoing immunotherapy. We categorized patients into pneumonitis (further subdivided into immunotherapy-induced, radiation-induced, and others) and non-pneumonitis groups. Using LIFEx software, we extracted 3D-radiomic features from both tumors and surrounding 1cm thick peritumoral regions. To address scanner-associated variations, a linear mixed-effect radiomics harmonization model was applied. A Random Forest algorithm was then used to develop a classification model predicting CIP occurrence based on the pre-treatment CT radiomic and clinical data. The dataset was split into training (70%) and test (30%) sets. The accuracy of predictions was evaluated using confusion matrix statistics and bootstrapping with 1,000 iterations for median and 95% confidence interval (CI). The performance metrics included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC). Results: 159 patients were analyzed, of which only 19 had checkpoint inhibitor-related pneumonitis. Among which, 17 (54.8%) patients had grade 1 pneumonitis, 12 (38.7%) patients had grade 2 pneumonitis, 2 (6.5%) patients had grade 3 pneumonitis, and none had grade 4 or grade 5 pneumonitis. The model achieved a sensitivity of 0.98 (0.97, 0.99), a specificity of 0.08 (0.05, 0.14), a PPV of 0.91 (0.90, 0.91), and an NPV of 0.33 ± 0.23 for predicting CIP. The AUC of 0.59 (0.56, 0.66) indicates that the model may predict checkpoint inhibitor-associated pneumonitis with an accuracy of 59%. Conclusions: The study provides insights into the potential of radiomic analysis coupled with AI algorithms and the use of harmonization model in predicting CIP among NSCLC patients treated with immunotherapy. However, larger studies are needed to validate our findings and the utility of harmonization models in predicting CIP.
Using machine learning for targeted advance care planning (ACP) conversations in patients with cancer: A quality improvement initiative.
Authors: Patel Mihir , Mara Alexandria , Acker Yvonne , Gollon Jamie , Wolf Steven , Zafar Yousuf , LeBlanc Thomas William , Ma Jessica 
Published: June 2024

1552 Background: Despite improvements, patients with advanced cancer often do not receive goal-concordant care at the end of life (EOL). Challenges with prognostication contribute to delays ACP conversations and result in more aggressive care, gaps in symptom management, and hospice underuse. Early identification of patients at high risk for mortality provides an avenue to implement targeted ACP conversations. Methods: We used a validated machine learning model that integrates demographics, lab values, vital signs, and medications to predict in-hospital, 30-day, and 6-month mortality risk for patients with a solid malignancy admitted from the emergency department (ED) to a dedicated solid malignancy oncology unit at Duke University Hospital. This unit includes one oncology and one palliative care attending physician, advance practice providers, and nurse coordinators. Providers received an email when a patient was identified as high risk for mortality. A pre-post study design compared ACP documentation and other EOL healthcare utilization before and after the notification intervention. The pre-intervention cohort included patients hospitalized from 1/7/2019 to 10/25/2019 and the post-intervention cohort from 9/19/2020 to 8/31/2021. We excluded patients admitted to the intensive care unit in the first 24 hours. We used chi-square or Fisher’s exact tests for categorical variables and Kruskal-Wallis tests for continuous variables; we stratified comparisons of categorical variables by physician division using Cochran-Mantel-Haenszel tests. Results: The pre-intervention cohort comprised 105 hospitalizations and 88 unique patients. Mean (SD) age was 64.9 (11.4); 60.0% (n=63) were White, 3.8% (4) Hispanic/Latino, and 65.7% (69) married. The post-intervention cohort comprised 84 hospitalizations and 77 patients. Mean (SD) age was 66.0 (12.2); 52.4% (44) were White, 2.4% (2) Hispanic/Latino, and 60.7% (51) married. Considering index hospitalizations, an ACP note was written for 2.3% (2) of hospitalizations pre-intervention vs. 80.5% (62) post-intervention (p <0.0001). This relationship held even if the physician was in palliative care (4.1% [2] vs. 84.6% [33]) or oncology (0% [0] vs. 76.3% [29]) (p <0.0001). Inpatient length of stay (LOS), hospice referral, code status change, ICU admission rate or LOS, 30-day readmissions, 30-day ED visits, and inpatient and 30-day deaths did not differ significantly between groups. Conclusions: Identification of hospitalized cancer patients with high mortality risk via machine learning led to a substantial increase in documented ACP conversations but did not impact EOL healthcare utilization. Further integration of the model in clinical practice is ongoing. Our intervention showed promise in changing clinician behavior, and additional work is needed to evaluate downstream impacts.
Performance of an artificial intelligence/machine learning model designed to identify hospitalized patients with cancer who could benefit from timely specialized palliative care delivery.
Authors: Strand Jacob J. , Morgan Alisha A , Pachman Deirdre R. , Storlie Curt B , Wilson Patrick M 
Published: June 2024

1559 Background: Timely access to specialty Palliative Care (SPC) services provides significant benefits to hospitalized patients with cancer, including improvements in quality of life. However, many patients with cancer who could benefit from SPC suffer from lack of timely referral or do not receive SPC services. A previously published trial of an artificial intelligence/machine learning (AI/ML) model to predict need for SPC at Mayo Clinic increased timely SPC consultation of hospitalized patients overall. We sought to review the performance of this algorithm in patients with cancer across an expanded hospitalized population. Methods: The study population consisted of all patients admitted into a Mayo Clinic hospital in Minnesota, Wisconsin, Arizona and Florida between January 2020 and September 2023. Due to the size of the cohort a case-control sample of three controls for every case was created. Patients were considered to be a patient with cancer if they had at least one billing diagnosis up to 1 year prior to their index hospitalization from any of the 5 HCC categories denoting cancer: Metastatic Cancer and Acute Leukemia (HCC 8); Lung and Other Severe Cancers (HCC 9); Lymphoma and Other Cancers (HCC 10); Colorectal, Bladder, and Other Cancers (HCC 11); and Breast, Prostate, and Other Cancers and Tumors (HCC 12). The training data set consisted of 107,076 patient encounters with a total of 8,355,090 time periods of constant risk. An AI/ML model using gradient boosting methods which contained 269 variables (both static and time-varying) of various classes with SPC consultation treated as a time-to-event outcome was trained. Results: Due to the longitudinal nature of the prediction, performance was assessed using the max score AUC (using the max score a patient received during a given encounter prior to an event or discharge) to produce the AUC. The model had an overall AUC of 0.932 (0.929, 0.935 – 95% CI). We saw a decrease in performance of the AI/ML model in the Oncology population with an overall performance of 0.818 (0.806, 0.83). Some of the most influential variables were previous SPC visit, age, metastatic disease, acute leukemia diagnosis, and pain scores. Conclusions: An AI/ML model can effectively predict the need for an inpatient PC consult in a population of hospitalized patients with cancer. [Table: see text]
Real world study of racial disparities associated with toxicities of sacituzumab govitecan.
Authors: Priyadarshini Shista , Petucci Justin , Katoch Avnish , Honavar Vasant , Vasekar Monali K. 
Published: June 2024

1572 Background: Sacituzumab govitecan (SG) is approved in the treatment of metastatic hormone receptor positive and triple negative breast cancer (BC). The efficacy of SG across racial subgroups is known but no real-world data compares the racial differences in toxicities. We aim to expand knowledge about racial disparities regarding SG toxicities and any ineffective treatment or drug failure. Methods: We utilized TriNetX, a multi-health care organization electronic health record database, to identify African American (AA) and Caucasian (C) cohort for outcome comparison. Inclusion criteria required a C50 ICD-10-CM diagnosis code and at least one SG treatment. Age, gender, and race-controlled Cox proportional-hazard (CPH) model was used to analyze the time until the first pause or cessation of SG (defined as first deviation from the approved 21-day SG cycle). Several measures of deviation were noted 0, ±1 and ±3 days. Association between race and toxicity outcomes (controlled for age and gender) for anemia, neutropenia, thrombocytopenia, nausea and inpatient encounters were evaluated using multiple logistic regression (MLR). The odds (OR) and hazard (HR) ratios with corrected p-values are reported as an effect size and significance estimation. Results: 823 patients met the inclusion criteria (545 C, 116 AA & 162 unknown). The mean age (in years) at the initiation of 1st SG treatment was 58.22 ± 12.7 for C and 58.19 ± 12.18 for AA. AA had a 1.26 times higher hazard of SG treatment pause/cessation as compared to C. Blacks experienced an 88% increase in the odds of having a delay longer than 7-days between 2 doses of 1st cycle SG. Males were found to have an 80% increase in the pause/cessation rate as compared to females. The median number of SG treatments was 6 (3 cycles) for both AA and C. SG was noted as 4th chemo line (median) among all races. When compared to C, AA had decreased odds for neutropenia, increased odds for anemia and increased odds for hospitalization. Hazard Ratio and corrected p-values for outcomes comparing the AA and C cohorts. Conclusions: Survival analysis indicates that AA and male patients have an increased SG treatment pause/cessation rate. While the result is sensitive to specific conditions used to calculate the deviation, an increased odds of delay in treatment is also found using logistic regression modeling. Additionally, AA experience increased odds for anemia and late-stage hospitalization. These findings highlight the impact of race on BC outcomes, treatment toxicities and the need for further research to enhance treatment outcomes in diverse populations. [Table: see text]