Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients

Ribelles, Nuria; Jerez, José M.; Rodríguez-Brazzarola, Pablo; Jiménez, Begoña; Díaz-Redondo, Tamara; Mesa, Héctor; Márquez, Antonia; Sánchez-Muñoz, Alfonso; Pajares, Bella; Carabantes, F.; Bermejo, Maria; Villar, Ester; Domínguez-Recio, María Emilia; Sáez, E; Gálvez, Laura; Godoy, Ana; Franco, Leo; Ruiz-Medina, Sofía; López, Irene; Alba, Emilio

doi:10.1016/j.ejca.2020.11.030

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…With the ability to examine more data efficiently, NLP is able to identify patterns and collect insights at a rate much faster than previously possible. In one study that deals with breast cancer treatment, NLP was used to analyze free form text from medical reports and develop predictive models for early and late progression to first-line treatment [ 20 ]. The best predictive model for early progression was able to achieve an area under the curve (AUC) of 0.758 using the NLP free-form text approach.…”

Section: Clinical Applications and Nlp Methods In Breast Imagingmentioning

confidence: 99%

Natural Language Processing for Breast Imaging: A Systematic Review

Diab

Deng

et al. 2023

Diagnostics

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Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field.

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Section: Clinical Applications and Nlp Methods In Breast Imagingmentioning

confidence: 99%

Natural Language Processing for Breast Imaging: A Systematic Review

Diab

Deng

et al. 2023

Diagnostics

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“…Sicheng Zhou et al and Xiaohui Zhang et al improved the BERT model to extract breast cancer phenotypes, in both English & Chinese text, and achieved F1 scores of 0.88 and 0.97 respectively. 82,83 NLP models were also used to analyse patients' clinical records for prognosis prediction, including breast cancer distant recurrence, 84 time to progression in first-line treatment of HER2-negative metastatic breast cancer, 85 and survival in metastatic breast cancers. 86 Although not in any clinical use, these prediction models could facilitate data collection for research, auditing, and perhaps eventually clinical decision making.…”

Section: Pathology Report Text Analysismentioning

confidence: 99%

Artificial intelligence in breast cancer histopathology

Chan

Cheng

et al. 2022

Histopathology

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This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists—namely, molecular and hormonal analysis, grading, mitotic figure counting, ki‐67 indexing, tumour‐infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open‐source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.

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“…Source documents such as pathology reports and some operative reports, which can also include staging information, are generally time consuming to capture manually and require consistent and considerable expertise to do so. A significant number of publications examining NLP as a solution to these data challenges have dealt with breast cancer where the creation of databases is ongoing [18,19,40,43,57,89,[91][92][93][94] and NLP has been particularly prolific in the breast cancer field representing 23.3 % of all NLP assisted medical research (closely followed by lung cancer with 14.56%) [64]. Many solutions however are not scalable.…”

Section: Clinical Historymentioning

confidence: 99%

“…The problem may be approached by addressing the individual aspects of unstructured [1,[19][20][21][22][23][24] clinical notes (e.g. history and physical exam) [25,26] , operative reports [3,27], pathology reports [28][29][30][31][32] and imaging reports [33][34][35][36][37][38][39] to analyze outcomes: response [21,[40][41][42], toxicity [43][44][45]and survival [33,46]. Natural Language Processing (NLP) has grown considerably in medicine to be employed for documentation [47,48], outcome prediction [1,27,43,49], phenotyping [50,51], data extraction [17,28,49,[52][53][54][55]…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

Krauze,

Camphausen

2022

Int J Bioinfor Intell Comput

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Oncology like most medical specialties, is undergoing a data revolution at the center of which lie vast and growing amounts of clinical data in unstructured, semi-structured and structed formats. Artificial intelligence approaches are widely employed in research endeavors in an attempt to harness electronic medical records data to advance patient outcomes. The use of clinical oncologic data, although collected on large scale, particularly with the increased implementation of electronic medical records, remains limited due to missing, incorrect or manually entered data in registries and the lack of resource allocation to data curation in real world settings. Natural Language Processing (NLP) may provide an avenue to extract data from electronic medical records and as a result has grown considerably in medicine to be employed for documentation, outcome analysis, phenotyping and clinical trial eligibility. Barriers to NLP persist with inability to aggregate findings across studies due to use of different methods and significant heterogeneity at all levels with important parameters such as patient comorbidities and performance status lacking implementation in AI approaches. The goal of this review is to provide an updated overview of natural language processing (NLP) and the current state of its application in oncology for clinicians and researchers that wish to implement NLP to augment registries and/or advance research projects.

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Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients

Cited by 16 publications

References 31 publications

Natural Language Processing for Breast Imaging: A Systematic Review

Natural Language Processing for Breast Imaging: A Systematic Review

Artificial intelligence in breast cancer histopathology

Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

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