Explainable clinical coding with in-domain adapted transformers

Background and Objective: Automatic clinical coding is a crucial task in the process of extracting relevant in-formation from unstructured medical documents contained in Electronic Health Records (EHR). However, most of the existing computer-based methods for clinical coding act as “black boxes”, without giving a detailed description of the reasons for the clinical-coding assignments, which greatly limits their applicability to real-world medical scenarios. The objective of this study is to use transformer-based models to effectively tackle explainable clinical-coding. In this way, we require the models to perform the assignments of clinical codes to medical cases, but also to provide the reference in the text that justifies each coding assignment. Methods: We examine the performance of 3 transformer-based architectures on 3 different explainable clinical-coding tasks. For each transformer, we compare the performance of the original general-domain version with an in-domain version of the model adapted to the specificities of the medical domain. We address the explainable clinical-coding problem as a dual medical named entity recognition (MER) and medical named entity normal-ization (MEN) task. For this purpose, we have developed two different approaches, namely a multi-task and a hierarchical-task strategy. Results: For each analyzed transformer, the clinical-domain version significantly outperforms the corresponding general domain model across the 3 explainable clinical-coding tasks analyzed in this study. Furthermore, the hierarchical-task approach yields a significantly superior performance than the multi-task strategy. Specifically, the combination of the hierarchical-task strategy with an ensemble approach leveraging the predictive capa-bilities of the 3 distinct clinical-domain transformersFunding for open access charge: Universidad de Málaga / CBUA. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga

Clinical text classification in Cancer Real-World Data in Spanish

Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.The authors acknowledge the support from the Ministerio de Ciencia e Innovación (MICINN) under project PID2020-116898RB-I00, from Universidad de Málaga and Junta de Andalucía through grants UMA20-FEDERJA-045 and PYC20-046-UMA (all including FEDER funds), and from the Malaga-Pfizer consortium for AI research in Cancer - MAPIC. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Detection of tumor morphology mentions in clinical reports in spanish using transformers

The aim of this study is to systematically examine the performance of transformer-based models for the detection of tumor morphology mentions in clinical documents in Spanish. For this purpose, we analyzed 3 transformer models supporting the Spanish language, namely multilingual BERT, BETO and XLM-RoBERTa. By means of a transfer- learning-based approach, the models were first pretrained on a collection of real-world oncology clinical cases with the goal of adapting trans- formers to the distinctive features of the Spanish oncology domain. The resulting models were further fine-tuned on the Cantemist-NER task, addressing the detection of tumor morphology mentions as a multi-class sequence-labeling problem. To evaluate the effectiveness of the proposed approach, we compared the obtained results by the domain-specific ver- sion of the examined transformers with the performance achieved by the general-domain version of the models. The results obtained in this pa- per empirically demonstrated that, for every analyzed transformer, the clinical version outperformed the corresponding general-domain model on the detection of tumor morphology mentions in clinical case reports in Spanish. Additionally, the combination of the transfer-learning-based approach with an ensemble strategy exploiting the predictive capabilities of the distinct transformer architectures yielded the best obtained results, achieving a precision value of 0.893, a recall of 0.887 and an F1-score of 0.89, which remarkably surpassed the prior state-of-the-art performance for the Cantemist-NER task

Validation of pain catastrophizing scale on breast cancer survivor

Introduction: Pain catastrophizing scale (PCS) is the most used scale to measure pain catastrophizing. In breast cancer survivors (BCS), pain catastrophizing is related to upper-limbs dysfunction and disability. This study aimed to assess the internal consistency, internal structure, and convergent validity of the Spanish version of the PCS in Spanish BCS. Material and Methods: Breast cancer survivors were recruited from the service of Medical Oncology of the University Clinical Hospital Virgen de la Victoria, in Málaga (Spain). The psychometric properties were evaluated with analysis factor structure by maximum likelihood extraction (MLE), internal consistency, and construct validity by confirmatory factor analysis (CFA). Results: Factor structure was three-dimensional, and one item was removed due to cross-loading. The new 12-item PCS showed a high internal consistency for the total score (α = 0.91) and a good homogeneity, and CFA revealed a satisfactory fit. PCS showed an acceptable correlation with FACS (r =0.53, p <0.01). Conclusion: Pain catastrophizing scale is a valid and reliable instrument to evaluate pain catastrophizing in Spanish BCS. This tool may help clinicians in the management of pain by assessing pain and by measuring the effect of interventions.This work was partially supported by Novartis Oncology [Contract N° PS16060 in IBIMA between Novartis-IBIMA, (Translation Research in Cancer B-01 & Clinimetric F-14)]. Funding for open access charge: Universidad de Málaga / CBU

Validation of the upper limb functional index on breast cancer survivor

Breast cancer survivors (BCS) may face functional alterations after surgical intervention. Upper Limb Disorders (ULDs) are highly prevalent even years after a diagnosis. Clinicians may assess the upper limbs after breast cancer. The Upper Limb Functional Index (ULFI) has been validated across different populations and languages. This study aimed to assess the psychometric properties of the Upper Limb Functional Index Spanish version (ULFI-Sp) in the BCS. Methods: A psychometric validation study of the ULFI-Sp was conducted on 216 voluntary breast cancer survivors. The psychometric properties were as follows: analysis of the factor structure by maximum likelihood extraction (MLE), internal consistency, and construct validity by confirmatory factor analysis (CFA). Results: The factor structure was one-dimensional. ULFI-Sp showed a high internal consistency for the total score (α = 0.916) and the regression score obtained from MLE (α = 0.996). CFA revealed a poor fit, and a new 14-item model (short version) was further tested. The developed short version of the ULFI-SP is preferable to assess upper limb function in Spanish BCS. Conclusions: Given the high prevalence of ULD in this population and the broader versions of ULFI across different languages, this study’s results may be transferred to clinical practice and integrated as part of upper limb assessment after breast cancer.Partial funding for open access charge: Universidad de Málag

Forearm Muscle Activity During the Handgrip Test in Breast Cancer Survivors: A Cross-Sectional Study

We recruited 102 breast cancer survivors at a secondary care in Malaga. Included breast cancer survivors showed a good upper limb functionality but a reduced forearm muscle activity. Forearm muscle activity showed a poor significant correlation with the cancer-related fatigue . Handgrip strength also showed a poor correlation with the upper limb functionality. Both outcomes tended to lower values with higher levels of cancer-related fatigue. Introduction/Background: Breast cancer survivors (BCS) frequently show upper limb dysfunctions. The forearm muscle activity measured by surface electromyography (sEMG) in this population has not been studied. This study aimed to describe forearm muscle activity in BCS, as well as to assess its possible relationship with other variables related to upper limb functionality and cancer-related fatigue (CRF). Materials and Methods: A cross-sectional study was carried out including 102 BCS as volunteers at a secondary care in Malaga, Spain. BCS were included if they were aged between 32 and 70 years old, without evidence of cancer recurrence at the time of recruitment. The forearm muscle activity (microvolts, μV) was assessed by sEMG during the handgrip test. The handgrip strength was assessed by dynamometry (kg), the upper limb functionality (%) was measured by the upper limb functional index (ULFI) question- naire and the CRF was also assessed by revised Piper Fatigue Scale (0-10 points). Results: BCS reported reduced forearm muscle activity (287.88 μV) and reduced handgrip strength (21.31 Kg), a good upper limb functionality (68.85%), and a moderate cancer-related fatigue (4.74). Forearm muscle activity showed a poor significant correlation (r = –0.223, P = .038) with the CRF. Handgrip strength showed a poor correlation with the upper limb functionality (r = 0.387, P < .001) and age (r=-0.200, P = .047)...Funding for open access charge: Universidad de Málaga /CBU

Subtipado molecular del cáncer de mama masculino con PAM50: Correlación con el subtipaje inmunohistoquímico y estudio de supervivencia.

Introducción: El cáncer de mama masculino es una enfermedad rara aún poco conocida, que principalmente corresponde a subtipo luminal usando la clasificación molecular subrogada a inmunohistoquímica. En este estudio, se evalúa por primera vez la correlación entre los subtipos moleculares basados en un panel inmunohistoquímico de seis marcadores y el obtenido mediante la firma PAM50 en el cáncer de mama masculino, así como la evolución clínica de los diferentes subtipos encontrados. Material y métodos: Se recogieron 67 muestras quirúrgicas de cáncer de mama masculino invasivo de cuatro diferentes Servicios de Anatomía Patológica. La tinción inmunohistoquímica se realizó sobre tissue-microarrays, con un panel de seis marcadores (RE, RP, Her2, Ki67, CK 5-6 y EGFR). Los subtipos de PAM50 se determinaron mediante nCounter Analysis System. Se estudió la asociación entre los subtipos obtenidos mediante inmunohistoquímica y los determinados por PAM50, así como la supervivencia global y la supervivencia libre de enfermedad en los diferentes subtipos de cada clasificación. Resultados: La distribución de los subtipos moleculares tumorales según PAM50 fue: 60% luminal B, 30% luminal A y 10% Her2-enriched. Sólo uno de los tumores Her2-enriched también fue detectado por inmunohistoquímica y tratado con trastuzumab. No se obtuvo ningún tumor de subtipo basal-like. Utilizando la clasificación inmunohistoquímica, 51% de los tumores fueron luminal B, 43% luminal A, 3,5% triple negativo y 1,5% Her2-positivo. Las características clínico-patológicas no difirieron significativamente entre los subtipos inmunohistoquímicos y PAM50. Se observó una supervivencia global menor en los tumores Her2-enriched comparados con los luminales. Conclusión: El cáncer de mama masculino es principalmente una enfermedad genómica luminal con un predominio del subtipo luminal B. Además, se observaron casos de pacientes Her2-negativos por inmunohistoquímica, pero de perfil Her2-enriched por PAM50, con peor evolución clínica comparado con el subtipo luminal, que podrían haberse beneficiado de terapia anti-Her2.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Human pregnane X receptor is expressed in breast carcinomas, potential heterodimers formation between hPXR and RXR-alpha.

The human pregnane X receptor (hPXR) is an orphan nuclear receptor that induces transcription of response elements present in steroid-inducible cytochrome P 450 gene promoters. This activation requires the participation of retinoid X receptors (RXRs), needed partners of hPXR to form heterodimers. We have investigated the expression of hPXR and RXRs in normal, premalignant, and malignant breast tissues, in order to det. whether their expression profile in localized infiltrative breast cancer is assocd. with an increased risk of recurrent disease. Methods: Breast samples from 99 patients including benign breast diseases, in situ and infiltrative carcinomas were processed for immunohistochem. and Western-blot anal. Results: Cancer cells from patients that developed recurrent disease showed a high cytoplasmic location of both hPXR isoforms. Only the infiltrative carcinomas that relapsed before 48 mo showed nuclear location of hPXR isoform 2. This location was assocd. with the nuclear immunoexpression of RXR-alpha. Conclusion: Breast cancer cells can express both variants 1 and 2 of hPXR. Infiltrative carcinomas that recurred showed a nuclear location of both hPXR and RXR-alpha; therefore, the overexpression and the subcellular location changes of hPXR could be considered as a potential new prognostic indicator

Development of a Novel NGS Methodology for Ultrasensitive Circulating Tumor DNA Detection as a Tool for Early-Stage Breast Cancer Diagnosis

Breast cancer (BC) is the most prevalent cancer in women. While usually detected when localized, invasive procedures are still required for diagnosis. Herein, we developed a novel ultrasensitive pipeline to detect circulating tumor DNA (ctDNA) in a series of 75 plasma samples from localized BC patients prior to any medical intervention. We first performed a tumor-informed analysis to correlate the mutations found in tumor tissue and plasma. Disregarding the tumor data next, we developed an approach to detect tumor mutations in plasma. We observed a mutation concordance between the tumor and plasma of 29.50% with a sensitivity down to 0.03% in mutant variant allele frequency (VAF). We detected mutations in 33.78% of the samples, identifying eight patients with plasma-only mutations. Altogether, we determined a specificity of 86.36% and a positive predictive value of 88.46% for BC detection. We demonstrated an association between higher ctDNA median VAF and higher tumor grade, multiple plasma mutations with a likelihood of relapse and more frequent TP53 plasma mutations in hormone receptor-negative tumors. Overall, we have developed a unique ultra-sensitive sequencing workflow with a technology not previously employed in early BC, paving the way for its application in BC screening.Comino-Mendez’s contract is funded by the Spanish Association Against Cancer Scientific Foundation (AECC). This study was supported by the “Consejería de Salud y Familias—Junta de Andalucía” (PI-0291-2019), “Fundación Unicaja” is funding Alba-Bernal’s contract and the Andalusia-Roche Network in Precision Medical Oncology Quirós-Ortega’s contract. Carbajosa-Antona’s contract is funded by the “Ayudas María Zambrano para la atracción de talento internacional—Universidad de Málaga”. Partial funding for open access charge: Universidad de Málag

Estudio de factores predictivos de respuesta patológica a quimioterapia neoadyuvante en el cáncer de mama receptores hormonales positivos HER2 negativo

Se trata de un estudio de casos de carcinomas de mama con escasa información respecto a los factores que determinan la repuesta a quimioterapia neoadyuvante. Un elevado nivel de proliferación y el estado de receptores hormonales han mostrado ser predictivos de dicha respuesta.La quimioterapia neoadyuvante (QTN) se utiliza cada vez más para conseguir una reducción tumoral que permita una cirugía conservadora y, mediante el grado de respuesta patológica tumoral, determinar el pronóstico de las pacientes. Los datos anatomopatológicos proporcionados por la biopsia previa al tratamiento (BAG), podrían ser determinantes para conocer el grado de respuesta y estar relacionados con la evolución de la enfermedad. El objetivo del estudio es, determinar el valor predictivo de respuesta, basado en variables anatomopatológicas de la BAG, en una cohorte de 220 casos de mujeres con cáncer de mama fenotipo RE positivo y HER2 negativo, tratadas de 3 a 6 meses con antraciclina/taxanos, en 4 hospitales de Andalucía, desde el año 2003 al año 2014. El tamaño previo tumoral, el grado histológico, RE (receptores de estrógenos), RP (receptores de progesterona), nivel de proliferación (Ki67) y subtipo molecular subrrogado inmunohistoquímico, fueron evaluados en la BAG, y se compararon con el grado de respuesta a QTN en la pieza quirúrgica (sistemas de Miller y Payne [MyP] y RCB) valorado por un observador. Resultados: En nuestro estudio, los tumores con inmunoexpresión de RE 50% (p<0,05). Además, responden mejor al tratamiento los carcinoma con subtipo luminal B que los luminal A (p=0,04). Conclusiones: Además de un elevado nivel de proliferación, el estado de receptores de estrógenos y progesterona también influye en la respuesta a QTN en carcinoma de mama Her2 negativo, respondiendo mejor los pacientes con expresión hormonal baja. Probablemente relacionado con lo anterior, las pacientes con subtipo luminal B, mostratron una mejor respuesta.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech