bluuebunny/crossref_metadata_2025_split huggingface dataset:
Strategies for implementation cancer care delivery studies in a community setting.
Authors: Chun Courtney , Braun-Inglis Christa M , Ferguson Valerie , Peterson Laura , Berenberg Jeffrey L. 
Published: June 2022

e13525 Background: Cancer care delivery Research (CCDR) is a multidisciplinary science that seeks to improve clinical outcomes and patient well-being by intervening on patient, clinician and organizational factors that influence care delivery. Many CCDR protocols also use implementation science to evaluate how best to introduce promising and previously established interventions into everyday care. These types of studies do not typically utilize research and clinic staff in the same ways as traditional treatment trials. One of the main goals of CCDR is for clinics to continue to use the intervention past the time of protocol participation. Even though these studies intend to improve cancer care delivery in the community, it is often difficult for community sites to embrace and prioritize CCDR due to their differences from typical treatment trials. Our recent observatory research highlights one Minority/Underserved (M/U) NCORPs key strategies for successful implementation of a decision aid CCDR trial in a community setting. Methods: HI M/U NCORP identified multiple steps prior to opening and enrolling patients onto a CCDR implementation trial at our community site. Step 1) Identifying an engaged physician and an advanced practice provider (APP) to be champions for the trial at the clinic. 2) Identifying and engaging key clinic staff and ensuring the workflow is feasible in the clinic. Key processes utilized during the entire course of the trial were also identified. 3) Research staff only coordinating and completing purely study related activities and not implementation activities. 4) Engaging other non-traditionally research involved administration staff and department management. 5) Utilizing ongoing clinic staff input to enhance trial success and optimize clinic workflow. Results: Using the strategies mentioned above, Hawai’i M/U NCORP was able to successfully open and enroll to an internet based CCDR implantation trial at a community clinic from 2019 through 2021. Not only were we able to achieve the highest accrual to a CCDR implementation trial to date within our NCORP, but also managed to do so at a clinic that had previously had minimal involvement in clinical trials in the past. Providers within the clinic received very positive reviews from their patients regarding the trial elements and the clinic has been working toward continuing the use of the material introduced by this CCDR implantation trial. Conclusions: Our successful use of these strategies described have not only been beneficial for our NCORP, but has made a large impact on improving the quality of clinic care at a busy community site. Our involved multi-step opening strategies set the stage for a seamless clinic workflow throughout the trial. This research has proven that these strategies can lead to the success of CCDR implementation studies in a community practice.
Multifactorial drug response modeling based on cancer organoid data.
Authors: Zhu Yitan , Brettin Thomas S. , Partin Alexander , Xia Fangfang , Shukla Maulik , Yoo Hyunseung , Cancino Andrea , Larsen Brian , Shaxted Jenna , Salahudeen Ameen , White Kevin , Stevens Rick L. 
Published: June 2022

e13544 Background: Prediction of drug response based on cancer molecular profiles is of paramount importance for precision oncology. Most existing drug response prediction models are built using drug screening data of immortalized cancer cell lines, which usually have altered genomic profiles compared with patient tumors. Recently, patient-derived organoids (PDOs) are emerging as a promising platform better representing patient tumors. We build computational drug response prediction models based on PDO drug screening data, which is the first study of its type to our knowledge. Methods: We successfully developed 27 PDO lines of colorectal cancer and 20 PDO lines of head and neck (H&N) cancer. Transcriptomics, copy number variation (CNV), and targeted DNA mutation data were generated for the PDO lines. The PDO lines were screened with 36 drugs of diversified mechanisms. The area under the dose response curve was taken as the response measurement. We used the LightGBM algorithm to build response prediction models based on cancer molecular data and drug chemical descriptors/fingerprints. To investigate the influence of different factors on the prediction performance, including different cancer types, cancer molecular features, drug features, data preprocessing methods, and others, we applied a multifactorial analysis scenario to build and evaluate 3,384 prediction models constructed with all possible combinations of the factors. For example, we built prediction models for H&N and colorectal PDOs separately and jointly. Results: A prediction model built for H&N PDOs achieved the highest prediction performance among all prediction models, which was R2 of 0.790 in 10-fold cross-validation. The model was built using drug descriptors, CNVs, and expressions of “landmark” genes well-representing cellular transcriptomic changes identified in the LINCS project. The table below includes all the factorial differences that caused an average R2 change larger than 1%. All R2 changes are statistically significant (p-values < 1×10–50), evaluated by pair-wise t-tests comparing models built with the status of the factor changed. The prediction performance increased, from colorectal cancer to two cancer types combined, and to H&N cancer. Gene expression data, either whole-transcriptome or the subset of LINCS genes, boosted the prediction performance. Between the two different dyes used to stain dead cells, TO-PRO-3 provided a higher prediction performance than Caspase-3/7. Conclusions: The highest drug response prediction performance achieved is R2 of 0.790. Cancer type, dye, and whether gene expressions are used in modeling are the factors most influential on prediction performance.[Table: see text]
QOPI Clinical Informatics: A digital platform to enable real-time quality reporting, clinical decision support, and rapid learning.
Authors: Williams Casey B. , Karagoz Kubra , Elsey Rachel , Meißner Tobias , Higashi Mitchell , Schadt Eric , Jun Tomi , Wang Xiaoyan , Chen Rong , Zhou Xiang , Guin Sunny , Lee John H. , Oh William K. 
Published: June 2022

e13545 Background: The ASCO Quality Oncology Practice Initiative (QOPI) was established in 2006 to facilitate quality measurement and continuous improvement of cancer care.1 ASCO and QOPI leadership encourage a continuous learning environment to develop new tools and methodologies that improve quality and maximize outcomes and value in oncology practice.2 Parallel advances in tumor molecular profiling, bioinformatics, and EMR data curation are adding new insights to support the real-world research of longitudinal patient journeys. Methods: Referencing the QOPI Certification Track 2021 Measures Summary, we employed data visualization techniques to create a precision oncology dashboard for breast cancer. This evaluation included 409 breast cancer patients for whom genomic testing was performed and presented at the Avera molecular tumor board. Patients were then matched to appropriate biomarker-based targeted therapies. Results: The dashboard identified and prioritized areas for enhanced data capture to inform strategies for care-coordination, research, and delivery of specialized care. Conclusions: An interactive, visual platform may help facilitate the integration of QOPI reporting into CancerLinQ, thereby fostering a real-time, bidirectional system for quality reporting, clinical decision support, and rapid learning.3,4 The next stage will (1) expand the tumor list; and (2) apply NLP methods to assess accuracy, outcomes and temporal reasoning to create interactive graphs for patient referral patterns and surgery decision drivers. References: 1. Neuss MN, Desch CE, McNiff KK, et al: A process for measuring the quality of cancer care: The Quality Oncology Practice Initiative. J Clin Oncol. 2005 2. Blayney DW, McNiff K, Eisenberg PD, Gilmore T, Jacobsen PB, Jacobson JO, Kadlubek PJ, Neuss MN, Simone J. Development and future of the American Society of Clinical Oncology's Quality Oncology Practice Initiative. J Clin Oncol. 2014 3. Blayney DW: Enhancing quality through innovation: American Society of Clinical Oncology presidential address 2010. J Clin Oncol. 2010 4. Schilsky R, Hauser R, Mann J, et al: Lessons learned from the development of the CancerLinQ prototype: Clinical decision support. J Clin Oncol. 31, 2013[Table: see text]
From patient generated data to precision medicine: An example of a patient-centered application in oncology.
Authors: Beutter Chantal N.L. , Martens Uwe Marc , Fegeler Christian 
Published: June 2022

e13559 Background: To practice precision medicine, the inclusion of patients in therapy is becoming increasingly important. Terms such as ‘patient empowerment’ (PE), ‘patient reported outcome’, and ‘patient generated data’ (PGD) are becoming quite present in current research. In order to implement PE consistently and integrate PGD in therapy, it is necessary to provide patients with specific tools. Methods: In order to develop such a corresponding system, challenges for implementation were identified as well as the current state of PGD in oncology. The aim was to design a system suitable for everyday use that allows the measurement of individual effects like quality of life (QoL) directly at the patient. Based on the positive experience of a previous developed questionnaire tool (EQU) to support physician-patient communication and the findings of the methodology a new patient-centered system was conceptualized with an automated data collection using sensor systems. A decisive factor was to support patients in self-management between treatment periods. This solution was then discussed extensively with stakeholders in order to identify and prioritize functionality and therefore incorporate specific user needs of oncological patients. Results: A great acceptance and need for tools to support patients especially between appointments was identified. In order to enable the recognition of individual characteristics, a continuous survey is essential. Based thereon an application called Lion-App for longitudinal QoL assessment in oncology was developed. A user-centered development aligns the app with the needs of the patients while HL7 FHIR provides the framework for an interoperable exchange. To reduce the burden of continuous interaction, sensors are used for data collection. With an interpretation of this data in the context of QoL, patients are able to visualize their personal course to gain an even better understanding of their disease. With the possibility to perform self-management between appointments, patients are lifted to another level of empowerment. In addition, the gained knowledge may then be used to focus on the effects of the disease in the next appointment and thus take a self-determined role in treatment. First evaluations of the app have been started. Conclusions: There are many ways to use PGD in the manner of precision medicine. The main factor is to create awareness among patients, collect data in an exchangeable way and involve end users early into development. A great opportunity to reduce user interactions and therefore enable long-term usage is the integration of sensor technologies. As a secondary use, algorithms can be extended via the expanded data base to become even more individualized to the patient. Thus, in the long run, such a system may help to further strengthen the cooperation between patient and physician and thus to enable a personalized, QoL-based treatment in oncology.
Drug response prediction in patient-derived xenografts with data augmentation and multimodal deep learning.
Authors: Partin Alexander , Brettin Thomas S. , Zhu Yitan , Shukla Maulik , Xia Fangfang , Yoo Hyunseung , Dolezal James M , Kochanny Sara , Pearson Alexander T. , Evrard Yvonne A. , Doroshow James H. , Stevens Rick L. 
Published: June 2022

e13572 Background: Prediction of drug response is a critical research area in precision oncology and has been previously explored with large drug screening studies of cancer cell lines (CCLs). Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics drug response of patients with cancer compared to CCLs. Methods: We investigate multimodal neural network (NN) and data augmentation for drug response prediction in PDXs. The multimodal NN learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs) where the multi-modality refers to tumor features only. The NN uses late integration where separate subnetworks are used to encode the input feature types before concatenation and prediction layers. Median tumor volume per treatment group is assessed relative to the control group to create a binary variable representing response. The data include twelve single-drug and 36 drug-pair treatments resulting in 2,556 single-drug and 2,203 drug-pair response values. Pathology and omics data from 487 PDXs from NCI's Patient Derived Models Repository are used as tumor feature model inputs. We explore whether the integration of WSIs with GE improves predictions as compared with models that use GE alone. We use two methods to address the limited number of response values in the dataset: 1) homogenize drug representations which allows to combine single-drug and drug-pairs into a single dataset, 2) augment drug-pair samples by switching the order of drug features which doubles the sample size of all drug-pair samples. These methods enable us to combine single-drug and drug-pair treatments which results in 6,962 responses, allowing us to train multimodal and unimodal NNs without changing architectures or the dataset. Results: Prediction performance of three unimodal NNs which use GE (um1, um2, and um3) are compared to assess the contribution of data augmentation methods. NN um1 that uses the full dataset which includes the original and the augmented drug-pair treatments as well as single-drug treatments significantly outperforms NNs (p-values < 0.01) that ignore either the augmented drug-pairs (um2) or the single-drug treatments (um3). In assessing the contribution of multimodal learning, results show that the multimodal NN (mm) outperforms both unimodal NNs that ignore either the GE (um4) or the WSIs (um1). However, the improvement of mm over um1 is not statistically significant (p-value < 0.26). Conclusions: Our results show that data augmentation and integration of histology images and GE can help improve prediction performance of drug response in PDXs.[Table: see text]
Using infrared spectroscopy to analyze breath of patients diagnosed with breast cancer.
Authors: Naz Farah , Groom Amy Grace , Mohiuddin MD , Sengupta Arpita , Daigle-Maloney Trisha , Burnell Margot J. , Michael James Charles Roger , Graham Stephen , Beydaghyan Gisia , Scheme Erik , Phinyomark Angkoon , Larracy Robyn 
Published: June 2022

e13579 Background: Population-level screening programs aimed at early detection and treatment of breast cancer saves lives. Analyzing breath using infrared spectroscopy offers a highly sensitive, non-invasive, and cost-effective mechanism for identifying exhaled volatile organic chemicals, and it is hypothesized that it may identify differences in the “breathprint” of women with breast cancer relative to those without a breast cancer diagnosis. Methods: Alveolar breath samples (10 L) were collected using a Breathe BioMedical alveolar breath sampler onto Tenax TA sorbent tubes. Corresponding room air samples (10 L) were collected in the same manner. Absorption spectra of the samples at a desorb temperature of 75 °C were measured by infrared cavity ring-down spectroscopy (IR-CRDS), a highly sensitive method of measuring absorption coefficients due to trace volatile organic compounds (VOCs) present in exhaled breath. After subtracting room air absorption and ordering each measured spectrum by increasing wavelength, missing values were imputed using spline interpolation. The absorption spectra were then normalized using one of four techniques: min-max, vector, peak or standard normal variate normalization. The first derivatives of the normalized absorption coefficients (187 values in total) were then used as features for discriminating samples from subjects with breast cancer and controls. The most useful features were selected based on minimum redundancy and maximum relevance (mRMR) and were used to train a linear support vector machine (SVM) classifier. Performance of classification models was estimated based on two data splitting configurations, non-nested leave-one-out cross-validation (LOOCV) and nested LOOCV. These approaches provide upper and lower bounds of expected model performance. Classification performance was used for tuning the number of features included in each model. Results: The analysis of this study is based on the spectra obtained from 70 participants (38 breast cancer and 32 controls), collected at the Saint John Regional Hospital in New Brunswick, Canada. Table below shows the non-nested and nested performance characteristics of classifiers with the best performing normalization technique. The number of features given for the nested model is not an integer as it indicates an average across the cross-validation folds. Conclusions: These results suggest that the classification of alveolar breath using IR-CRDS is a promising technique for the detection of breast cancer. Performance of classification models. AUC is the area under the receiver operator characteristics curve.[Table: see text]
The need for artificial intelligence curriculum in medical education: A Canadian cross-sectional study of future oncology trainees.
Authors: Pucchio Aidan , Rathagirishnan Raahulan , Caton Natasha , Gariscsak Peter , Del Papa Joshua , Vo Vicky , Lee Wonjae , Nabhen Jacqueline J. , Ynoe de Moraes Fabio 
Published: June 2022

e13583 Background: Emerging artificial intelligence (AI) technologies have diverse applications in medicine, with early evidence suggesting that AI tools can accurately perform key tasks in oncology. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study seeks to assess exposure to AI in medical education among trainees interested in pursuing a career in oncology, and the need for AI education in medicine. Methods: A 32 question survey for Canadian undergraduate medical students was distributed to students at all 17 Canadian medical schools. The survey assessed the currently available and perceived need for learning opportunities about AI and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale (LS) survey questions were scored from 1 (disagree) to 5 (agree), and analyzed using a two-sided one sample t-test vs a neutral value. Interview transcripts were analyzed using qualitative thematic analysis. Results are described as mean LS score ± standard deviation. Results: We received 486 responses from 17 of 17 medical schools. Of these respondents, 98 (20.2%) are willing to pursue a residency in an oncology-related field (pathology, radiology, general surgery, internal medicine, radiation oncology). Respondents agreed that AI applications in medicine would become common in the future (3.80±0.38) and would improve medicine (3.71±0.54). Further, respondents agreed that they would need to use and understand AI during their medical careers (3.76±0.572; 3.43±0.773), and that AI should be formally taught in medical education (3.43±0.756). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (1.76±0.785) and that AI-related learning opportunities were inadequate (2.12±0.802). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and logistical challenges in adding AI to curriculum. Conclusions: A lack of educational opportunities about AI in medicine were identified across Canadian medical students. Given that medical students overwhelmingly believe that AI is important to the future of medicine, and AI tools are currently progressing towards clinical implementation, AI should be considered for inclusion in formal medical curriculum.
Utilizing Natural Language Processing (NLP) to identify breast cancer associated-lung metastases from pathology reports to delineate characteristics and challenges of this common site of breast cancer recurrence.
Authors: Valentín López José Carlos , Ho Alice Y. , Moy Beverly , Isakoff Steven J. , Juric Dejan , Ellisen Leif W. , Peppercorn Jeffrey M. , Bardia Aditya , Hughes Kevin S. , Vidula Neelima 
Published: June 2022

e13592 Background: NLP (artificial intelligence) can automate the identification of records in large datasets. The purpose of this study was to evaluate the feasibility of NLP to identify breast cancer-associated lung metastases to understand clinical characteristics and challenges posed by this common site of breast cancer recurrence. Methods: Patients with pathologically confirmed breast cancer associated-lung metastases seen at a large academic center between 3/2012-5/2019 were identified using NLP of institutional pathology reports, with an IRB approved protocol. Chart review was performed to confirm breast cancer associated-lung metastases and determine clinical and pathological features. Results: Using NLP, 32 patients with pathology reports denoting breast cancer associated-lung metastases were identified, with pathologic confirmation of lung biopsy tissue in the majority of cases (24), and pleural fluid specimens (8) on the remainder. Ten of 32 (31%) were HR+/HER2-, 3/32 (9.3%) HER2+, and 19/32 (59%) TNBC. The majority were invasive ductal carcinoma (21/26) with the remainder invasive lobular carcinoma (2/26) or mixed histology (3/26). Median age at lung metastasis diagnosis was 62 years (range 31-88). The median time to development of lung metastasis following primary breast cancer was 5.6 years (range 0-24.8 years). Fifty six percent of lung metastases were detected on imaging and 44% by symptoms including dyspnea, cough, or pain. Tumor tissue genotyping results on the lung metastases were available for 8 patients showing PI3KCA (5), TP53 (3), SMARCA4 (2), ERBB2 (1), FGFR3 (1), ATM (1), CDK4 (1), MYC (1), and ESR1 (1). Treatment after diagnosis of lung metastases included hormone therapy (61%), chemotherapy (84%), lung irradiation (26%), and surgical resection of lung metastases (6%). Lung metastases were associated with considerable morbidity including pleural effusion (15%), dyspnea (6%), pneumothorax (3%), hemothorax (3%), and atelectasis (3%). Patients diagnosed with lung metastases had brain (32%), bone (35%), renal (6%), skin (3%) and adrenal (3%) metastases during disease course. Conclusions: NLP can help identify organ specific metastases from pathology reports, such as breast cancer associated-lung metastases, which can then facilitate observational, translational, and clinical research to characterize and address challenges posed by this common site of breast cancer recurrence. This cohort of patients highlights the morbidity of breast cancer associated-lung metastases and potential role of NLP for disease characterization and clinical research. (Support from ASCO Medical Student Rotation for Underrepresented Populations Award.)