tbuckley/cabot-search huggingface dataset:
Utility of PET-CT for assessment of response to neo-adjuvant chemotherapy in breast cancer.
Authors: Deepthi Valiyaveettil, Monica Malik
Journal: Journal of Clinical Oncology (2024)

12 Background: Response to neo-adjuvant chemotherapy (NACT) is associated with prognosis in non-metastatic breast cancer and can potentially inform further therapeutic options. The aim of this study was to correlate post NACT response on PET CT with pathological response. Methods: All patients who underwent pre- NACT (baseline) and post NACT PET CT imaging with F-18 FDG in a dose of 5-7 MBq/kg body weight with a minimum of 185 MBq given intravenously were retrospectively analyzed. Scan was performed on GE Discovery 710 whole-body PET scan with 128 slice CT. Relevant clinical and imaging related details were extracted from the treatment records. Post NACT PET response (PERCIST criteria) in the primary tumor and nodes was correlated with the pathological response on the post surgical specimen. Analysis was done using Microsoft Excel and SPSS v20 (IBM SPSS). Results: The median age was 49 years (range 30-76 years). 83.1% of patients had node positive disease on baseline PET imaging (not pathologically confirmed). Post chemotherapy PET showed complete metabolic response in the primary tumor and regional nodes in 56.3% and 74% of patients respectively. Two patients had progressive local disease. Pathological complete response rates post surgery in triple negative, Her2 neu positive and ER positive group was 50%, 34% and 12% respectively. The sensitivity, specificity, positive predictive value and negative predictive value is tabulated below. Conclusions: Overall FDG PET CT showed good specificity with limited sensitivity for post NACT response assessment in breast cancer. Our results suggest that post NACT PET CT may have limited utility for clinical decision making and at present its use cannot be recommended outside of a clinical trial.[Table: see text]
Effects of Naphtho[2,1- <i>a</i> ]pyrene Exposure on CYP1A1 Expression: An <i>in Vivo</i> and <i>in Vitro</i> Mechanistic Study Exploring the Role of m6A Posttranscriptional Modification
Authors: Jiemiao Shen, Li Wang, Wen Zhang, Xing Gong, Sheng Li, Xuyan Zou, Chao Chen, Rong Xia, Di Zhang, Shuyu Xu, Jiayi Xu, Shaozhuo Wang, Yinyue Jiang, Hong Sun, Chao Wang, Shou-Lin Wang
Journal: Environmental Health Perspectives (2024)

Currently, many emerging polycyclic aromatic hydrocarbons (PAHs) have been found to be widely present in the environment. However, little has been reported about their toxicity, particularly in relation to CYP1A1.
Implementation of hippocampal sparing whole brain radiotherapy in a resource-limited setting.
Authors: David Dai-Wee Lee, Zulaikha Jamalludin, Nur Fadhlina Abdul Satar, Gwo Fuang Ho
Journal: Journal of Clinical Oncology (2024)

31 Background: Whole brain radiotherapy (WBRT) is a common treatment for brain metastases, but it often leads to cognitive decline due to hippocampal damage. Hippocampal avoidant technique has emerged to mitigate this cognitive toxicity. Our institution began hippocampal-avoidant WBRT (HA-WBRT) in 2021. We report our experience in implementing this technique in a resource-limited setting. Methods: We reviewed the medical records of patients who underwent HA-WBRT in our institution between January 2021 and March 2024. Clinical characteristics, treatment details, and dosimetry were reviewed. Hippocampal sparing was achieved using volumetric-modulated arc therapy (VMAT). Results: A total of 11 patients were included in the analysis. The median age was 57 years old, with a male-to-female ratio of 3:8. Most patients had metastatic breast cancer (7/11) followed by lung cancer (2/11). Among the breast cancer patients, 5 of them had HER2 positive subtype. Ten patients had good performance status (ECOG 0). Most patients (8/11) were treated with 20Gy in 5 fractions (with or without boost to tumor), and 3 patients with 30Gy in 10 fractions. The median time from CT simulation to treatment was 20.5 days. The target volume and organ-at-risk contouring were performed as per the AROMA trial (20Gy in 5 fractions) or RTOG 0933 study (30Gy in 10 fractions). The median beam-on time was 5.61 minutes. Nine patients completed WBRT with minimal acute toxicities. Two patients did not complete treatment due to a decline in performance status. Most patients were treated in multiple institutions thus, follow-up data was scarce. Conclusions: In our experience, HA-WBRT is feasible for suitable patients. Patients that benefit from this technique are younger, have good performance status, and have longer life expectancy e.g. breast primary. The challenges of implementing this treatment are a longer time for contouring and treatment planning, and longer treatment time compared with 2D or 3D WBRT. Long-term follow-up is needed to validate the benefit of this technique. Patient selection is key in implementing HA-WBRT in a resource-limited setting.
Autologous bone marrow transplant in standard-risk newly diagnosed multiple myeloma: A systematic review.
Authors: Andree Kurniawan, Dimas Priantono, Tubagus Djumhana Atmakusuma, Chandra Sari, Devi Astri Rivera Amelia, Deden Djatnika, Muhammad Arman Nasution, Nia Novianti Siregar, Nugraheny Prasasti Purlikasari, Farieda Ariyanti, Beta Agustia Wisman, Patricia Angel Tjhai, Angela Giselvania, Felix Wijovi, Devina Adella Halim, Rivaldo Steven Heriyanto
Journal: Journal of Clinical Oncology (2024)

174 Background: Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Bone marrow transplants in high-risk multiple myeloma have been a standard of care for newly diagnosed multiple myeloma (NDMM). Autologous bone marrow transplant (ABMT) has emerged as a promising therapeutic approach for standard risk of NDMM. We aim to review the existing literature on the efficacy of ABMT in standard-risk NDMM in comparison with non-transplant therapeutic approaches. Methods: We conducted a systematic search across PubMed, Google Scholar, Science Direct, and Embase for studies within the last 10 years. We included studies that compared ABMT with other therapies without any history of prior transplants in NDMM patients. We excluded studies that retrospective and case studies. We extracted firstly using PICO: standards risk NDMM, ABMT upfront therapy, progression-free survival, and toxicity. The quality of the study was assessed using the Newcastle-Ottawa scale or JADAD scale questionnaire. Results: Our search yielded 7 studies, with a total of 3728 patients in 7 randomized controlled trial studies. The endpoint was mainly progression-free survival (PFS), with others being response rates and stringent complete response (sCR). All studies consistently showed that ABMT yielded significantly better PFS and response rates in NDMM, with high-dose melphalan being the most common induction regime. ABMT resulted in relatively more severe toxic side effects compared to drugs only. However, the safety profile of ABMT is considered favorable, with manageable adverse events. Induction methods before ABMT include lenalidomide, bortezomib, dexamethasone, carfilzomib, and melphalan, with some studies reporting follow-ups on further maintenance therapy, mainly with lenalidomide. The effectivity of ABMT remains consistent regardless of the drugs used. Conclusions: ABMT is a favorable therapeutic approach for standard risk NDMM with manageable adverse effects and an acceptable safety profile. The effectivity of ABMT is regardless of the concomitant drug used.
Integrating lifestyle medicine into cancer care: A new paradigm.
Authors: Jasmin Hundal, Nicole Sirotin
Journal: Journal of Clinical Oncology (2024)

179 Background: Amid rising global cancer rates and obesity-related cancer risks, oncology must blend traditional cancer treatments with evidence-based lifestyle medicine (LM) to enhance patient outcomes. LM—evidence-based interventions in nutrition, exercise, sleep, avoiding substances, and social connection —emerges as a critical tool in the care spectrum. ASCO and ACS guidelines incorporating lifestyle interventions are limited, and existing ones have notable implementation gaps. This study advocates for integration of LM into cancer care. Such interventions, supported by growing research, offer significant promise in reducing cancer treatment side effects and improving outcomes, highlighting an urgent need for their incorporation into oncological protocols. Methods: The Department of Preventive Medicine at Cleveland Clinic Abu Dhabi (CCAD) launched an LM program at the Oncology Institute. Oncologists refer to LM physicians who assess patients using an LM-validated questionnaire. Cardiometabolic risk factors: body composition, waist circumference, lipid profile, hemoglobin A1c, high-sensitivity CRP, liver function, and liver elastography are tested at baseline and 3 months. Patients receive personalized LM treatment by certified physicians, dieticians, physiatrists, and psychologists, aimed at reducing treatment side effects and enhancing cancer outcomes while improving cardiometabolic health. Follow-ups at 6 weeks and 3 months post-intervention ensure progress tracking and reassessment. Results: Initiated in January 2021, it began as a pilot program for breast cancer patients and has since expanded to all cancer patients. To date, it enrolled 425 patients, evidencing sustainability and acceptance within the patient community. Referral rates from oncologists have shown a steady increase, reflecting the program's growing credibility and utility. Patient satisfaction levels are reported to be high. Preliminary outcomes indicate improvements in both physical and psychological health metrics, suggestive of the program's comprehensive impact. Conclusions: The pioneering oncology LM program at CCAD established a transformative model that seamlessly integrates LM into traditional oncology care and acts as a bridge to survivorship. By ensuring sustained access to lifestyle interventions from the point of diagnosis through post-treatment, the program aligns with the latest research and ASCO/ACS guidelines, pioneering a new standard in cancer treatment. It can effectively address cardiometabolic risks, including obesity, and reduce treatment-related adverse outcomes. This program demonstrates the feasibility and a replicable global model, one that merges evidence-based lifestyle practices with standard oncological care to create a unified approach that could be adopted by healthcare systems worldwide, marking a significant step in the evolution of patient-centered cancer care.
Stereotactic centralized ablative radiation therapy: A novel methodology in treating bulky tumor and its technical realization.
Authors: Jun Yang, Weisi Yan, Qiuxia Lu, Liangfu Han, Brian Wang
Journal: Journal of Clinical Oncology (2024)

159 Background: Bulky tumors pose significant challenges to traditional treatment modalities such as surgery, chemotherapy, and conventional radiotherapy. This study introduces a novel therapeutic approach named as Stereotactic-Core-Ablative-Radiation-Therapy (SCART), designed to deliver an ablative dose to a substantial core of the bulky tumor while quickly decreasing to a lower dose at the tumor periphery. Methods: A SCART-Treatment-Volume (STV) at the core of the GTV was predefined to administer an ablative radiation dose, aiming to induce DNA damage in cancer cells and potentially trigger biological cancer-killing effects. Utilizing Linac-based VMAT or Cyberknife techniques with 6MV photon beams, multiple radiation fields intersect and optimize at STV, generating an ablative dose at the STV. The dose rapidly diminishes to a safe level at the edge of GTV sparing the surrounding tissue. In the phase-1 trial, nineteen patients with 21 bulky tumors enrolled, receiving SCART at five dose levels (15GyX1, 15GyX2, 15GyX3, 18GyX3, 21GyX3, and 24GyX3), while maintaining the GTV's peripheral dose at 5Gy each fraction. Results: All patients completed treatment with average beam-on time of 8.9min and average treatment time of 18.5min. Mean follow-up time is 15.4 month. No grade-III or higher toxicity was observed. 7/19 patients still survive, with the overall survival of 40% at 30 months. Mean tumor volume shrinks by 60% between initial 301cc and post-SCART volumes of 118cc. Long-term follow-up revealed that 14/21 tumors achieved Partial Response, 2/21 Complete-Response, 3/21 Stable-Disease, and 1/21 Progressive-Disease, leading to an encouraging local control of 95%. Conclusions: SCART emerges as a safe and effective strategy for treating bulky malignant tumors, demonstrating excellent local control and overall survival. Multiple treatment courses were feasible. The results from phase-1 study suggest that SCART could revolutionize the treatment landscape for bulky tumors, offering a promising avenue for further exploration and application in clinical practice.
OncoGPT: An AI assistant for genomic-driven precision oncology.
Authors: Samuel Ding, Xiao-Ming Xing, Yan Ding, Qin Huang
Journal: Journal of Clinical Oncology (2024)

160 Background: Cancer patient’s unique genomic profile can help oncologists select a course of precise personalized treatment for the subject. High throughput next-generation sequencing (NGS) technology allows us to identify genomic alterations simultaneously within a patient’s tumor tissue and blood-circulating tumor DNA (ctDNA) in a single test, however, robust tool for accessing mutation-treatment matching is limited. Methods: OncoGPT was built on a cloud-based elastic computing platform. The NGS data analytical module consists of a cascade of computational algorithms for NGS data processing and gene variant calling. The interactive and univariate Cox proportional hazards models were used for mutation-treatment matching and prognostic effect analysis, respectively. Machine learning algorithms including decision tree, random forest, and neural network were trained and tested with novel features for tissue-of-origin (TO) classification across 8 caner types. Results: We built an AI-driven NGS data analytical platform by integrating computational models, matching algorithms and variant annotation databases to highly accurate achieve cancer-related gene mutations, copy number variation, and structure variants using NGS data from 35,122 tumors across 8 cancer types from three institutions. Next, an AI-driven TO classifier was developed and achieved a weighted F1 score of 0.926 for high confidence predictions (≥ 0.9) on tumor samples. Furthermore, augmented AI matching algorithms were applied to match the optimal personalized treatment and provide prognostic prediction for cancer patients with significantly bett survival outcomes (hazard ratio (HR) = 0.326; 95% confidence interval (CI) = 0.213–0.565; P = 2.52×10−5). Conclusions: We have successfully developed an innovative intelligent system (OncoGPT) with AI capabilities to help accurately find actionable targets from patient tumor or blood ctDNA NGS sequencing data and precisely match individualized therapeutic and clinical trial options for patients. In addition, OncoGPT classified primary tumor sites across 8 different cancer types with high confidence predictions. We believe that OncoGPT would help clinicians make optimal treatment decisions for cancer patients through genomic-driven precision oncology.
Voice-based depression assessment in patients with gynecological cancer.
Authors: Nozomi Higashiyama, Ken Yamaguchi, Maki Umemiya, Yoshihide Inayama, Ayami Koike, Akihiko Ueda, Rin Mizuno, Mana Taki, Koji Yamanoi, Ryusuke Murakami, Junzo Hamanishi, Masaki Mandai
Journal: Journal of Clinical Oncology (2024)

161 Background: Although depression in patients with cancer can lead to a deterioration of treatment adherence and quality of life (QOL), diagnosis of depression in patients with cancer is difficult due to symptoms overlapping with side effects of cancer treatment such as fatigue. Traditional screening tools like the PHQ-9 questionnaire are underutilized in clinical practice. Recently, interest has grown in utilizing patients' voices to assess their mental well-being. This study aims to evaluate depression in patients with gynecological cancer and explore the usefulness of voice analysis as a depression detection tool. Methods: PHQ-9 scores were collected from 197 patients with gynecological cancer, with 28 cases undergoing longitudinal assessments at diagnosis, postoperatively, and during chemotherapy. Voice recordings were obtained from 70 patients at diagnosis, alongside serum samples from 22 patients. The PHQ-9 scores were compared across different treatment periods, with a specific focus on patients exhibiting a PHQ score of ≧10, who can be diagnosed with Major Depressive Disorder (MDD). The provision of supportive care for MDD patients was also verified. A random forest model was developed from voice features obtained from 70 patients, with hyperparameter tuning and cross-validation to predict mild depression. Serum metabolites were comprehensively analyzed between depression prediction group and normal prediction group by the depression prediction model. Results: Mean PHQ-9 scores decreased over time since diagnosis; initially 6.50 ± 4.99 (mean ±SD), post-operatively 6.18 ± 4.25, and during chemotherapy 4.88 ± 4.29. The frequencies of MDD also decreased over the clinical course; at diagnosis 24.3%, post-operatively 15.5%, and during chemotherapy 15.4%. Out of 17 cases of MDD during chemotherapy, only one (6.25%) received psychiatric intervention. All MDD cases during chemotherapy had PHQ-9 scores of 5 or higher, representing mild depression at diagnosis. Mild depression at diagnosis could be predicted with an AUC of 0.89 using voice features. Metabolites identified by the voice-based depression prediction model were associated with depression, such as xanthine, methionine, and taurocholic acid. Conclusions: Depression during chemotherapy is often not intervened. Patients with mild depression at diagnosis tend to develop MDD during chemotherapy. Voice-based mental health assessment at diagnosis is possibly a promising screening tool for depression during subsequent cancer treatment. Clinical trial information: UMIN000044266 .
Collaborative data sharing: The International Acute Myeloid Leukemia Consortium (INTERACT).
Authors: Daisuke Tomizawa, Kirk D. Wyatt, E. Anders Kolb, Dirk Reinhardt, Sarah K Tasian, Jeffrey E Rubnitz, C Michel Zwaan, Enal Hindi, Todd Allen Alonzo, Shiro Tanaka, Brian Furner, Suzi Birz, Arnaud Petit, Michael Watkins, Robert B Gerbing, Reo Tanoshima, Stanley B. Pounds, Bhavya Achen, Samuel Louis Volchenboum, Todd Michael Cooper
Journal: Journal of Clinical Oncology (2024)

169 Background: Outcomes for children and adolescents/young adults (AYAs) with acute myeloid leukemia (AML) remain suboptimal and are decidedly poor for patients with subtypes defined by high-risk features, including canonical genetic alterations and relapsed/refractory presentation. Due to their rarity, these subtypes are difficult to study within the existing clinical trials paradigm. Data sharing efforts aim to increase our understanding of rare cancer subtypes by pooling data from multiple sources. The INTERnational Acute myeloid leukemia ConsorTium (INTERACT) was established to pool and share data on pediatric and AYA patients with AML. Methods: In 2019, a data dictionary was created through an iterative, consensus-driven process that analyzed pre-existing clinical trials case report forms as the initial basis. By developing this standardized representation for data on patients with AML, data could be harmonized and integrated. A memorandum of understanding was signed in 2021, formally establishing INTERACT. An executive committee was convened to establish scientific priorities, facilitate ongoing data dictionary work, usher agreements with data contributors and review data requests submitted by investigators. In January 2024, the initial set of AML data was released on the Pediatric Cancer Data Commons (PCDC) data portal. Results: As of April 2024, the data dictionary includes 157 standardized data elements that were used to harmonize clinical data on 3,413 patients who participated in seven clinical trials conducted by four pediatric oncology cooperative groups across the United States, Europe, and Asia. Elements in the data dictionary include demographics, initial disease characteristics, genetic alterations, and survival status. Aggregate data can be freely explored using the publicly-available PCDC data portal (portal.pedscommons.org) and investigators seeking line-level access to data may submit a project request to the INTERACT executive committee for consideration. Conclusions: International collaborative data-sharing efforts will advance our understanding of pediatric and AYA AML by reducing barriers that constrain our study of AML to smaller cohorts of data. We hope that an early outcome of data sharing will be to increase our understanding of optimal treatments for patients with relapsed/refractory disease. Future goals of INTERACT include identifying data from additional studies to include, establishing relationships with new data contributors, and establishing working groups to advance the consortium’s scientific and clinical trials goals.
Retrieval-augmented large language models for clinical trial screening.
Authors: Jianqiao He, Shanglei Gai, Si Xian Ho, Shi Ling Chua, Viviana Oo, Ma Wai Wai Zaw, Daniel Shao-Weng Tan, Ryan Tan
Journal: Journal of Clinical Oncology (2024)

157 Background: Clinical trial screening is currently manual and laborious. We tested several large language models enhanced with retrieval-augmented generation (RAG-LLM) to assess their performance on this task. Methods: We extracted eligibility criteria of 184 oncology trials with FDA approval notifications between 8 January 2020 and 18 January 2024, as well as information on cancer staging and performance status scoring for the RAG-LLM vector database. A medical oncologist and 2 senior clinical trial coordinators developed a test set of 975 synthetic patient profiles which included primary site, stage, prior therapy, tumor mutations and one additional clinical feature. Each profile was paired with one of the 184 trials and annotated for ground-truth eligibility and the reason for it. This process was repeated for another validation set of 240 longer and more challenging profiles paired with 8 ongoing trials. We developed RAG-LLMs with 4 leading LLMs (Zephyr-7B, Med42, GPT 3.5, GPT4) and evaluated their accuracy in determining trial eligibility as well as retrieval-augmented generation assessment (RAGAs) metrics. A response was deemed accurate only if it correctly assigned both trial eligibility and the reason for assignment. Results: GPT4 performed best and achieved an accuracy of 95.18% and 80.00% on the test and validation set respectively with a mean inference time of 10.95 seconds. It also demonstrated the highest answer relevancy, context relevancy and faithfulness (Table). Conclusions: Our results demonstrate potential for RAG-LLMs to assist with trial screening at scale. Further evaluation in real-world cohorts utilizing electronic records and full protocol data with tracking of impact on trial enrolment can be explored within secure firewalls. [Table: see text]