tbuckley/cabot-search huggingface dataset:
MET exon 14 skipping in NSCLC: An unMET Indian need.
Authors: Ullas Batra, Shrinidhi Nathany, Mansi Sharma, Amrith Patel, Anurag Mehta, Sakshi Mattoo, Vanita Noronha, Kumar Prabhash
Journal: Journal of Clinical Oncology (2024)

e13537 Background: The approval of capmatinib and tepotinib, selective MET tyrosine kinase inhibitors (TKI), has further shifted the therapeutic paradigm of advanced NSCLC. MET exon 14 skipping (MET ex14) alteration is a complex mechanism, reported in ~3-4% cases of NSCLC. Controlled trials have reported remarkable outcomes of selective MET TKIs, however real world outcomes from this part of the world are currently lacking. Methods: This is a non-interventional study reporting real world outcomes of NSCLC with MET ex14 from two apex cancer centers of India. 49 eligible patients were recovered from the electronic medical record archives. All cases with MET ex14 were included irrespective of clinicopathologic characteristics and therapy offered. Statistical analysis was performed using R studio. Results: Clinical characteristics are shown (Table). Treatment patterns were heterogeneous across therapy lines and included chemotherapy, MET TKIs and immune checkpoint inhibitors. 42/49 patients opted for treatment; rest dropped out owing to economic restraints. 1st line TKI was offered to 14 patients, and chemotherapy to 28 patients. 1st line PFS of TKI treated was 11.9 months (95%CI:5.8-17.8) vs. 5.9months (95%CI: 3.9-16.8) (p&lt;0.002*) for the chemotherapy treated cases (HR for TKI: 0.3295 (95%CI: 0.1630 to 0.6663). Presence of TP53 co-mutation and male gender and were factors affecting 1st line PFS (p&lt;0.06) on multivariate analysis. 2nd line treatment was opted by 13/14 patients in TKI treated group of which 5 received sequential TKI, and 8 received chemotherapy and 22/28 in chemotherapy treated group (TKI: 14, chemotherapy: 8). PFS for 2nd line TKI was 7.7 months (95%CI: 2.8-14.8) vs 4.6 months (95%CI:2.1-9.8) for chemotherapy {HR for TKI: 0.3641 (95%CI: 0.1378 to 0.9616)} (p&lt;0.04*). The dropout rate from 1st to 2nd line was 36% and 44% in subsequent line. 28 (57%) received MET TKI at any given point in disease course. Median OS for patients treated with 1st line TKI was not reached at study end (95%CI: 9.2-NR) and was 20.7 months (95%CI:18.1-36.1) for chemotherapy (p=0.3). One year and 2 year survival rates for 1st line TKI and Chemotherapy were 86%vs 73% and 47% vs 45%, respectively. Conclusions: This is the largest cohort from India reporting real world uptake of TKI therapy in MET ex14 altered NSCLC. There is a blaring disparity with respect to accessibility of drugs, as well as prevailing socioeconomic restraints. This study clearly depicts an unmet need for more global clinical trials for rare genomic alterations as well as patient access programs in this part of the world. [Table: see text]
Using an AI trial screening tool to assess language as a barrier for enrollment of underrepresented minorities in cancer clinical trials.
Authors: Laura Corvese, Christina Weiss, Sharon Huie, Bin Zhou, Yanhong Deng, Guannan Gong, Pamela L. Kunz, Neal A. Fischbach
Journal: Journal of Clinical Oncology (2024)

e13586 Background: A major limitation of clinical trials is limited generalizability due to low enrollment of underrepresented minorities (URM). The percentage of patients who are non-English speaking (NES) is higher in URM and language barriers may discourage providers to offer participation in clinical trials. We have developed an artificial intelligence-based system to identify patients eligible for clinical trials, Clinical Trials Patient Matching (CTPM). This platform offers an ideal opportunity to explore bias in enrolling patients on clinical trials (CT). Among patients identified by CTPM as eligible for CT we assessed the impact of NES on trial enrollment. Methods: Individual algorithms were constructed within CTPM to identify patients potentially eligible for two trials open at Yale Cancer Center, DARE (NCT04567420) and EMBER-4 (NCT05514054). Eligibility was confirmed by Clinical Trials Office staff and notification of eligibility was communicated to the treating provider. Retrospectively, we evaluated the impact of NES status on clinical trial enrollment. This analysis occurred between 9/1/2022 and 11/30/2023 and all study-related activities were approved by the Yale IRB. Results: Out of the 13,466 unique patients presenting to the Yale Cancer Center and Smilow Cancer Network breast program within the period of study, 5.1% were NES and 94.9% were English speaking (ES). Of the patients identified by CTPM as eligible for the 2 clinical trials 7.9% were NES and 92.14% were ES. However, out of patients enrolled, only 1.59% were NES and 98.41% were ES. For the DARE trial, enrollment rate was 38.3% for ES vs 0% for NES (p=0.0081). For the EMBER4 trial, enrollment was 13.4% for ES vs 14.3% for NES (p=1.0). The aggregate data for both trials revealed an enrollment rate of 29.4% for ES vs 5.6% for NES (p=0.0285). Conclusions: We found that patients who identify as NES were less likely to be enrolled in cancer clinical trials. CTPM appears to be an effective tool to prospectively identify underrepresented populations in clinical trials and serve as a platform for developing interventions to address disparities in trial participation, including language. [Table: see text]
Clinicians' experiences and perspectives of a web-based lung cancer referral pathway.
Authors: Zulfiquer Ali Otty, Sabe S. Sabesan, Sarah Larkins, Rebecca Evans, Amy Brown
Journal: Journal of Clinical Oncology (2024)

e13539 Background: Efficient and streamlined referral of patients with suspected lung cancer is an integral part of providing optimal care for these patients. There is emerging evidence that use of online care pathways, such as HealthPathways is associated with improved referral quality from primary care, more timely access to secondary care and standardization of clinical management decisions by general practitioners (GPs). The Townsville Lung Cancer Referral Pathway (TLCRP) is part of ' Townsville HealthPathways', which is the evidence-based clinical pathway portal used in the Townsville Hospital and Health service in northern Australia. This study aimed to explore the experiences and perspectives of clinicians about TLCRP. Methods: This was a descriptive qualitative study nested within a larger project evaluating TLCRP, utilising a broader implementation science framework. In-depth, semi-structured interviews of GPs and lung cancer specialists were conducted. An iterative, inductive thematic analysis of interview transcripts was undertaken to derive key codes and themes regarding participant experiences and perceptions. Results: Twenty interviews were conducted, 14 with GPs and six with specialists across disciplines of respiratory medicine, oncology, and cardiothoracic surgery. Of the GPs, 11 were practicing in the regional locality of Townsville and three practiced in surrounding rural areas. Interview analysis identified two major themes and several sub-themes (Table). Several enablers and barriers to implementing TLCRP were identified. Barriers to adaptation of TLCRP included lack of clinical time, resistance to changing referral patterns, lack of familiarity or experience with HealthPathways and technology issues. Enablers included clinical usefulness of TLCRP, support from HealthPathways implementing team, integration of TLCRP with the electronic referral system and positive feedback from specialists. Conclusions: Emerging themes from this study may be used to reduce the barriers and improve uptake of TLCRP and other health care pathways in the local health service. Improving the uptake of TLCRP may mean that more patients in our region experience a streamlined journey to more timely specialist lung cancer care and improved health outcomes. [Table: see text]
A transfer learning-based graph neural network model using mIF images to predict neoadjuvant immunochemotherapy response in patients with gastrointestinal cancer.
Authors: Shenglong Li, Yanan Wang, Lan Su, Jun Wang, Jiaolin Zhou
Journal: Journal of Clinical Oncology (2024)

e13592 Background: Neoadjuvant immunochemotherapy has great potential to revolutionize the treatment of gastrointestinal cancer. However, effective biomarkers to classify responders from non-responders are still lacking. We aimed to build a response prediction model using multiplex immunofluorescence (mIF) images from patients with gastrointestinal cancer. Methods: We proposed a graph neural network-based (GNN) approach based on the pre-treatment mIF samples collected from 77 patients, including 30 gastric cancer (GC) patients, 34 colorectal cancer (CRC) patients, and 13 esophageal cancer (ESCA) patients. All the tissue samples were stained by two panels of mIF agents, namely Panel-A (CD3, CD8, PD1, PD-L1) and Panel-B (CD56, CD68, CD163). Cell coordinates, agent densities, and cell morphology features were used to generate cell graphs for model training. Results: Initially, we trained a pan-cancer GNN model for each panel and assessed its performance on the patch- and patient-level using five-fold cross-validation. The Panel-A model achieved higher areas under the receiver operating characteristic curve (AUROC) (patch-level: 0.71 ± 0.036, patient-level: 0.65 ± 0.010) than the Panel-B model (patch-level: 0.59 ±0.017, patient-level: 0.62 ± 0.021) and conventional mIF-based markers (i.e., positive cell ratios/counts of fluorescence agents, AUROC: 0.34 ~ 0.54). Specifically, the performance of CRC was better in the Panel-A model (patch-level: 0.75± 0.053, patient-level: 0.76 ± 0.053) than that in Panel-B (patch-level: 0.52 ± 0.027, patient-level: 0.60 ± 0.028), while the performance of GC was better in Panel-B (patch-level: 0.64 ± 0.030, patient-level: 0.71 ± 0.034) than that in Panel-A (patch-level: 0.53 ± 0.019, patient-level: 0.51 ± 0.018). The AUROCs of both panels of ESCA were less than 0.5, reflecting feature differences associated with treatment response between ESCA and GC/CRC. Furthermore, discrepancies of GC and CRC between Panel-A and -B motivated us to further optimize the performance for individual cancer types using transfer learning (TL). The GC TL-model performance (patch-level: 0.691±0.025, patient-level: 0.751 ± 0.046) was improved compared with the pan-cancer model, but not for the CRC TL-model (patch-level: 0.636 ± 0.066, patient-level: 0.697 ± 0.039). Moreover, the best patient-level specificity and sensitivity under the optimal Youden-Index were 70% and 88% for CRC, and 93% and 62% for GC. Conclusions: The GNN-based approach using mIF images demonstrated promising potential in predicting the immunochemotherapy response of gastrointestinal cancer patients, despite the limited number of samples were curated. The performance variation among cancer types indicated the cancer heterogeneity and the necessity of building single-cancer models when more data becomes available.
Rectal cancer: Demographics and healthcare outcomes in a minority-rich community hospital.
Authors: Michelle Cholankeril, Rachel Sankhe, Flavio Eduardo Salinas, Andrew Polyak, Keia Smith
Journal: Journal of Clinical Oncology (2024)

e13735 Background: Rectal cancer (RC) has seen a decreasing incidence and mortality due to national screening guidelines, innovative therapies and new technology. In recent years, however, we have noted shifts in its demographics pertaining to variation in stage at diagnosis and mortality rates amongst various ethnic groups. Minority populations, namely Hispanic (H) and Black (B) patients (pts), continue to see higher incidence, diagnosis at more advanced stages and poorer survival than White (W) pts. In B pts, mortality rates are higher with an increased likelihood of distant-stage RC creating a significant survival disparity. Current studies involving H and Asians(A) show similar disparities. Compared to other malignancies, RC has improved survival for pts when diagnosed in a timely fashion and therefore, we seek to identify potential patterns within the diverse population we care for. Methods: Trinitas Medical Center RWJBH is an urban, community hospital that predominantly treats a minority population. With institutional IRB approval, we completed a retrospective chart review of 59 pts diagnosed with Stage I-IV RC from 2017- 2023. Pts were treated at our cancer center. We assessed age and stage at diagnosis and demographics such as age, sex and ethnicity. Results: We reviewed 59 pts with the following ethnic distribution: 22 H, 21 W, 11 B and 5 A pts. The median age at diagnosis with RC was 57 in H, 64 in B, 71 in W 71. More males were diagnosed in all groups except A pts. B pts were found to be diagnosed with more advanced disease: 36% at Stage III, 45% at Stage IV. W pts also saw a similar trend with 33% at Stage III and 24% at Stage IV. Our H pts had comparatively fewer distant stage diagnoses: 23% at Stage III, 18% at Stage IV. Common sites of metastasis in B: liver 27%, lung 27%, locoregional lymph nodes 9%. In W pts: liver 24%, locoregional lymph nodes 24%, lung 10% and brain 5%. In H pts, 10 of 22(45%) were diagnosed without metastatic disease, though of the 10 who did, 2 had brain involvement. Mortality was highest in B pts at 55%, 33% for W, 20% for A and 18% for H pts. Conclusions: In our study, we note H pts being diagnosed at an earlier median age, at earlier stages of disease, with higher survival rates. Our B pts were diagnosed younger than W pts, at later stages of disease, with higher mortality rates. Brain involvement, which usually is rare, was seen in both H and W upon recurrence. Despite having a multi specialized cancer center, we note limitations in our access to screening resources and the availability of colorectal surgical expertise to enhance care for our underserved population. The challenge of disentangling disparities within RC will benefit from improved healthcare access and availability of multimodality resources in this disease state where cure is the ultimate goal.
Racial and elderly disparities in receipt of guideline-concordant care in stage I-III breast cancer.
Authors: Brenda Sofia Castillo, Taussia Boadi, Xiaoyan Han, Lawrence N. Shulman, Yehoda M. Martei
Journal: Journal of Clinical Oncology (2024)

e13688 Background: Elderly and non-Hispanic Black (NHB) patients have worse breast cancer specific survival outcomes. We evaluated whether guideline concordant care (GCC) and all-cause mortality in stage I-III breast cancer differed by race among patients ≥ 65 years old. Methods: We used the National Cancer Database (NCDB), to identify a cohort of 25174 NHB and 233357 non-Hispanic White patients (NHW), ≥65 years old, diagnosed with stage I-III breast cancer between 2010 and 2019. We used the National Comprehensive Cancer Network guidelines to define GCC, including surgery, radiation and systemic therapy. Specific systemic therapies and cause of death were unavailable. We used logistic regression models to assess the association between race and GCC. We used Cox proportional hazards models to estimate associations between race and all-cause mortality after adjusting for GCC. Both models adjusted for disease, demographics, income and educational attainment. We assessed the odds of not receiving timely GCC within 30, 60 and 90 days from diagnosis for NHB vs. NHW patients using logistic regression. Results: Overall, 15% of patients did not receive GCC. Being NHB compared to NHW was associated with increased odds of not receiving GCC in univariate (OR=1.24 95%CI: 1.20-1.28) and multivariate analysis (aOR=1.13, 95%CI: 1.08-1.17). Being NHB was associated with 26% increased risk of all-cause mortality in the univariate analysis (HR=1.26, p&lt;0.001), which decreased to 4% increased risk, after adjusting for GCC, age, clinical and sociodemographic factors (aHR=1.04, p=0.027). Non-receipt of GCC (aHR=1.75, p&lt;0.001) and lower educational attainment (aHR 1.18, p &lt;0.001) were independently associated with higher risk of all-cause mortality. Higher median income (aHR 0.91, p&lt;0.001) was independently associated with a decreased risk of mortality. Being NHW compared to NHB was associated with increased odds of initiating treatment within 30 (OR=1.65, 95% CI 1.6-1.69), 60 (OR=2.11, 95% CI 2.04-2.18), and 90 days of breast cancer diagnosis (OR=2.39, 95% CI 2.27-2.51). Conclusions: Among elderly patients with stage I-III BC, being NHB was associated with increased odds of not receiving GCC and less timely treatment initiation. Being NHB was associated with increased all-cause mortality, which was attenuated after adjusting for GCC. Optimizing timely receipt of GCC may reduce inferior survival outcomes among elderly NHB breast cancer patients. Identifying area-level determinants may help design targeted interventions. Limitations include lack of granular systemic therapy data, likely overestimating GCC receipt, biasing the analysis towards the null, and lack of cause-specific death among elderly patients with competing risks of death.
Implementing large language model-based artificial intelligence (AI) technology in proposing effective treatment plans in patients with cancer.
Authors: Aditya Santoki, Rachel Jones, Manas Peter, Aju Mathew
Journal: Journal of Clinical Oncology (2024)

e13660 Background: Language Learning Models (LLM), are programs that can respond to queries without previous exposure to the input. This is because the programs are actually generative mathematical models trained on hundreds of terabytes of textual data. The output is thus a statistical assessment of the corpus of human generated text likely indicating the correct answer. ChatGPT is an example of an LLM that can understand questions and synthesize responses based on probability. However Chat GPT’s potential to accurately assist physicians with a diagnosis, remains yet to be determined. The goal of this study is to elucidate AI’s potential in proposing both feasible and accurate treatment plans specifically for patients with cancer. Methods: Fifty patients at a medical oncology practice were recruited for this study. For each patient, the physician’s shorthand notes were recorded and organized. ChatGPT was then asked to come up with a potential treatment plan using these notes according to a predetermined set workflow for each individual. The responses were then reviewed by a medical oncologist for accuracy and feasibility via a qualtrics survey. Results: ChatGPT correctly proposed a treatment plan in agreement with the physician for a total of 18 cancer patients allowing for the agreement rate to be 36%. For the rest of the 32 patients, the physician disagreed with the proposed ChatGPT treatment plan on the basis of lack of personalization, inhibitive cost, incorrect recommendation, and greater alternative options. Thirteen patients fell into the ‘Lack of Personalization in Treatment' category, twelve in the ‘Incorrect Recommendation’ category, four in ‘Inhibitive Cost' category, and three in the ‘Greater Alternative Options’ category. Most patients however fell in the category of personalization, suggesting ChatGPT’s unfamiliarity with the subject of generating personal treatment plans. Conclusions: In the current study, ChatGPT was expected to create a treatment plan with only the clinician’s abridged shorthand notes and without any familiarity with the physician’s personal preferences for treatment. This knowledge gap can account for an agreement rate less than 50%. Despite its flaws, ChatGPT was, however, able to generate an adjusted and corrected version of the treatment plan when prompted further outside the set workflow. This highlights the invaluable role of clinical experience in synthesizing an accurate treatment plan. With its encroachment into the medical field, ChatGPT has shown potential in proposing feasible and accurate treatment plans, especially in the oncology clinical setting. Despite the low agreement rate obtained from this study, ChatGPT has shown promise in developing an agreeable treatment plan with the oversight of the attending physician.
Racial and ethnic disparities in hospital outcomes following hospitalizations for mastectomy for breast cancer.
Authors: Ana Cristina Sandoval-Leon, Yolcar Chamorro, Muni Rubens, Mukesh Roy, Lauren Carcas, Naomi Dempsey, Manmeet Singh Ahluwalia, Reshma L. Mahtani
Journal: Journal of Clinical Oncology (2024)

e13691 Background: Racial and ethnic disparities in breast cancer (BC) treatment and survival have been well documented. White (W) and Asian (A) women are more likely to have localized BC (BC) than Black (B) or Hispanic (H) women. Furthermore, the incidence of triple negative BC is higher in B women who also have the highest death rates from BC. In this study, we aimed to identify an association between race/ethnicity and outcomes following hospitalization for mastectomy using a large national database. Methods: This study isa retrospective analysis of the National Inpatient Sample database, the largest all-payer inpatient database in the United States, of hospitalizations between 2016-2019. Patients (pts) ≥18 years of age, admitted for mastectomy for BC were included and stratified by race/ethnicity. The outcomes of the study were in-hospital mortality rate, prolonged hospital length of stay, surgical site infection, bleeding complication, cardiac complication, respiratory complication, acute kidney injury, urinary tract infection, and wound dehiscence. Hospitalizations and outcomes were identified using ICD-10 diagnosis and procedure codes. Logistic regression analysis was done to identify an association between race and outcomes. Results: A total of 302,208 pts who were hospitalized for mastectomy were included. Of these 218,092 (72.2%) pts were W, 36,242 (12.0%) were B, 25,130 (8.3%) were Hispanics, and 22,744 (7.5%) were of another race. Among B pts, adverse outcomes were significantly higher (p&lt;0.001), including in-hospital mortality rate (0.2%), prolonged hospital length of stay (64.1%), surgical site infection (0.8%), bleeding complication (4.2%), cardiac complication (0.8%), respiratory complication (0.3%), and acute kidney injury (1.4%). After adjusting for various factors including age, insurance, hospital region, hypertension, diabetes, obesity, peripheral vascular disease, alcohol use, Elixhauser comorbidity index, whether pts underwent axillary lymph node dissection and others, logistic regression was performed. This analysis identified that B pts had significantly higher odds for mortality (adjusted odds ratio [aOR], 2.99, 95% CI: 1.62-5.73), prolonged hospital length of stay (aOR, 1.32, 95% CI: 1.18-1.49), and bleeding complication (aOR, 1.28, 95% CI: 1.13-1.48). Conclusions: Results of our study show that adverse hospital outcomes following mastectomy for BC were significantly higher among Black patients. These outcomes remained significant after adjusting for clinical covariates, raising the possibility of other contributing factors to these differences in outcomes. Study limitations include the lack of information on stage at diagnosis, receptor status, and that Hispanics were categorized under race. Further studies are needed to develop effective interventions to reduce these disparities.