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1.
Sci Total Environ ; 750: 141252, 2021 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-33182174

RESUMEN

Anthropogenic activities are seriously endangering the conservation of biodiversity worldwide, calling for urgent actions to mitigate their impact on ecosystems. We applied machine learning techniques to predict the response of freshwater ecosystems to multiple anthropogenic pressures, with the goal of informing the definition of water policy targets and management measures to recover and protect aquatic biodiversity. Random Forest and Gradient Boosted Regression Trees algorithms were used for the modelling of the biological indices of macroinvertebrates and diatoms in the Tagus river basin (Spain). Among the anthropogenic stressors considered as explanatory variables, the categories of land cover in the upstream catchment area and the nutrient concentrations showed the highest impact on biological communities. The model was then used to predict the biological response to different nutrient concentrations in river water, with the goal of exploring the effect of different regulatory thresholds on the ecosystem status. Specifically, we considered the maximum nutrient concentrations set by the Spanish legislation, as well as by the legislation of other European Union Member States. According to our model, the current nutrient thresholds in Spain ensure values of biological indices consistent with the good ecological status in only about 60% of the total number of water bodies. By applying more restrictive nutrient concentrations, the number of water bodies with biological indices in good status could increase by almost 40%. Moreover, coupling more restrictive nutrient thresholds with measures that improve the riparian habitat yields up to 85% of water bodies with biological indices in good status, thus proving to be a key approach to restore the status of the ecosystem.


Asunto(s)
Ecosistema , Ríos , Monitoreo del Ambiente , Aprendizaje Automático , España , Agua
2.
Kardiologiia ; 60(9): 46-54, 2020 Oct 14.
Artículo en Ruso | MEDLINE | ID: mdl-33131474

RESUMEN

Aim        To compare assessments of epicardial adipose tissue (EAT) volumes obtained with a semi-automatic, physician-performed analysis and an automatic analysis using a machine-learning algorithm by data of low-dose (LDCT) and standard computed tomography (CT) of chest organs.Material and methods        This analytical, retrospective, transversal study randomly included 100 patients from a database of a united radiological informational service (URIS). The patients underwent LDCT as a part of the project "Low-dose chest computed tomography as a screening method for detection of lung cancer and other diseases of chest organs" (n=50) and chest CT according to a standard protocol (n=50) in outpatient clinics of Moscow. Each image was read by two radiologists on a Syngo. via VB20 workstation. In addition, each image was evaluated with a developed machine-learning algorithm, which provides a completely automatic measurement of EAT.Results   Comparison of EAT volumes obtained with chest LDCT and CT showed highly consistent results both for the expert-performed semi-automatic analyses (correlation coefficient >98 %) and between the expert layout and the machine-learning algorithm (correlation coefficient >95 %). Time of performing segmentation and volumetry on one image with the machine-learning algorithm was not longer than 40 sec, which was 30 times faster than the quantitative analysis performed by an expert and potentially facilitated quantification of the EAT volume in the clinical conditions.Conclusion            The proposed method of automatic volumetry will expedite the analysis of EAT for predicting the risk of ischemic heart disease.


Asunto(s)
Algoritmos , Aprendizaje Automático , Tejido Adiposo/diagnóstico por imagen , Humanos , Moscú , Estudios Retrospectivos
3.
Nat Commun ; 11(1): 5668, 2020 11 09.
Artículo en Inglés | MEDLINE | ID: mdl-33168827

RESUMEN

Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to model transportability. Here, we leverage the US PCORnet platform to develop an AKI prediction model and assess its transportability across six independent health systems. Our work demonstrates that cross-site performance deterioration is likely and reveals heterogeneity of risk factors across populations to be the cause. Therefore, no matter how accurate an AI model is trained at the source hospital, whether it can be adopted at target hospitals is an unanswered question. To fill the research gap, we derive a method to predict the transportability of AI models which can accelerate the adaptation process of external AI models in hospitals.


Asunto(s)
Lesión Renal Aguda/etiología , Inteligencia Artificial , Aprendizaje Automático , Lesión Renal Aguda/sangre , Adolescente , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Curva ROC , Medición de Riesgo , Factores de Riesgo , Adulto Joven
4.
Environ Monit Assess ; 192(12): 761, 2020 Nov 14.
Artículo en Inglés | MEDLINE | ID: mdl-33188607

RESUMEN

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011-2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.


Asunto(s)
Monitoreo del Ambiente , Ríos , Australia , Predicción , Aprendizaje Automático , Redes Neurales de la Computación
5.
BMC Bioinformatics ; 21(1): 493, 2020 Oct 31.
Artículo en Inglés | MEDLINE | ID: mdl-33129275

RESUMEN

BACKGROUND: Cytokines act by binding to specific receptors in the plasma membrane of target cells. Knowledge of cytokine-receptor interaction (CRI) is very important for understanding the pathogenesis of various human diseases-notably autoimmune, inflammatory and infectious diseases-and identifying potential therapeutic targets. Recently, machine learning algorithms have been used to predict CRIs. "Gold Standard" negative datasets are still lacking and strong biases in negative datasets can significantly affect the training of learning algorithms and their evaluation. To mitigate the unrepresentativeness and bias inherent in the negative sample selection (non-interacting proteins), we propose a clustering-based approach for representative negative sample selection. RESULTS: We used deep autoencoders to investigate the effect of different sampling approaches for non-interacting pairs on the training and the performance of machine learning classifiers. By using the anomaly detection capabilities of deep autoencoders we deduced the effects of different categories of negative samples on the training of learning algorithms. Random sampling for selecting non-interacting pairs results in either over- or under-representation of hard or easy to classify instances. When K-means based sampling of negative datasets is applied to mitigate the inadequacies of random sampling, random forest (RF) together with the combined feature set of atomic composition, physicochemical-2grams and two different representations of evolutionary information performs best. Average model performances based on leave-one-out cross validation (loocv) over ten different negative sample sets that each model was trained with, show that RF models significantly outperform the previous best CRI predictor in terms of accuracy (+ 5.1%), specificity (+ 13%), mcc (+ 0.1) and g-means value (+ 5.1). Evaluations using tenfold cv and training/testing splits confirm the competitive performance. CONCLUSIONS: A comparative analysis was performed to assess the effect of three different sampling methods (random, K-means and uniform sampling) on the training of learning algorithms using different evaluation methods. Models trained on K-means sampled datasets generally show a significantly improved performance compared to those trained on random selections-with RF seemingly benefiting most in our particular setting. Our findings on the sampling are highly relevant and apply to many applications of supervised learning approaches in bioinformatics.


Asunto(s)
Receptores de Citocinas/metabolismo , Algoritmos , Humanos , Aprendizaje Automático , Posición Específica de Matrices de Puntuación , Reproducibilidad de los Resultados
6.
Nat Commun ; 11(1): 5454, 2020 11 03.
Artículo en Inglés | MEDLINE | ID: mdl-33144581

RESUMEN

Molecular tagging is an approach to labeling physical objects using DNA or other molecules that can be used when methods such as RFID tags and QR codes are unsuitable. No molecular tagging method exists that is inexpensive, fast and reliable to decode, and usable in minimal resource environments to create or read tags. To address this, we present Porcupine, an end-user molecular tagging system featuring DNA-based tags readable within seconds using a portable nanopore device. Porcupine's digital bits are represented by the presence or absence of distinct DNA strands, called molecular bits (molbits). We classify molbits directly from raw nanopore signal, avoiding basecalling. To extend shelf life, decrease readout time, and make tags robust to environmental contamination, molbits are prepared for readout during tag assembly and can be stabilized by dehydration. The result is an extensible, real-time, high accuracy tagging system that includes an approach to developing highly separable barcodes.


Asunto(s)
ADN/genética , Nanoporos , Biología Sintética/métodos , Algoritmos , Biología Computacional , Sistemas de Computación , Procesamiento Automatizado de Datos , Aprendizaje Automático , Análisis de Secuencia de ADN
7.
Environ Monit Assess ; 192(12): 759, 2020 Nov 12.
Artículo en Inglés | MEDLINE | ID: mdl-33184748

RESUMEN

In recent years, indirect methods have been used to estimate soil salinity in agricultural lands. In this research, the electrical conductivity of 93 soil samples from 0 to 30 cm and 0 to 100 cm was measured using the hypercube technique at Sharifabad-Saveh Plain, Iran. Land area parameters such as TWI, TCI, STP, DEM, and LS were used as topographic variables and spatial indices of salinity and vegetation were derived from Landsat 8 images. Soil salinity off crops and gardens was determined at 0-30 cm and 0-100 cm. The data were divided into two series: the training set (70%) and the test set (30%). In order to model and predict salinity, models such as an artificial neural network (ANN), integration of neural network and genetic algorithm (ANN-GA), PLSR, and decision tree (DT) were used. The results of the models' evaluation based on MSE and R2 indices showed that the ANN-GA model has the highest accuracy in predicting soil properties. This model improved the accuracy of soil salinity prediction by 28%, 42%, and 23% in 0-30 cm and by 20%, 28%, and 25% at 100 cm than ANN, PLSR, and DT. The result showed the 2 dS/m EC at alfalfa and cucurbits farmlands while pistachio orchards have low salinity and bare lands have moderate and high salinity.


Asunto(s)
Salinidad , Suelo , Monitoreo del Ambiente , Irán , Aprendizaje Automático
8.
BMC Infect Dis ; 20(1): 825, 2020 Nov 11.
Artículo en Inglés | MEDLINE | ID: mdl-33176716

RESUMEN

BACKGROUND: Light microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and involve sophisticated hardware components, which makes it hard to deploy them in resource-limited areas. RESULTS: We designed an Android mobile application called Malaria Screener, which makes smartphones an affordable yet effective solution for automated malaria light microscopy. The mobile app utilizes high-resolution cameras and computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, smear image analysis, and result visualization in its slide screening process, and is equipped with a database to provide easy access to the acquired data. CONCLUSION: Malaria Screener makes the screening process faster, more consistent, and less dependent on human expertise. The app is modular, allowing other research groups to integrate their methods and models for image processing and machine learning, while acquiring and analyzing their data.


Asunto(s)
Procesamiento de Imagen Asistido por Computador/métodos , Malaria Falciparum/diagnóstico por imagen , Tamizaje Masivo/métodos , Microscopía/métodos , Plasmodium falciparum/aislamiento & purificación , Teléfono Inteligente , Exactitud de los Datos , Humanos , Aprendizaje Automático , Malaria Falciparum/parasitología , Sensibilidad y Especificidad , Programas Informáticos
9.
BMC Bioinformatics ; 21(1): 505, 2020 Nov 07.
Artículo en Inglés | MEDLINE | ID: mdl-33160303

RESUMEN

BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear. RESULTS: In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation. CONCLUSIONS: Our results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.


Asunto(s)
Trastorno del Espectro Autista/genética , Aprendizaje Automático , ARN Largo no Codificante/metabolismo , Trastorno del Espectro Autista/patología , Encéfalo/crecimiento & desarrollo , Encéfalo/metabolismo , Humanos , ARN Largo no Codificante/genética , Riesgo , Transcriptoma
11.
Gan To Kagaku Ryoho ; 47(10): 1399-1404, 2020 Oct.
Artículo en Japonés | MEDLINE | ID: mdl-33130728

RESUMEN

With the development and diversification of medical care, the importance of precision medicine, which selects a suitable treatment for the individual patient from a huge number of options, is increasing. It is often difficult to explain multifactorial diseases such as cancer and chronic inflammatory diseases by a single hypothesis. In such case, a data-driven approach is essential to construct individualized models based on comprehensive observation of the target disease. The data-driven approach utilizes artificial intelligence to extract, predict, and classify patterns of data, considering different types of variables and complex dependencies between variables. In this paper, we introduce the basic idea, typical methods, and application examples of artificial intelligence and its core technology, machine learning. We would like to discuss a new framework of medical research toward the next generation medicine, while reviewing how machine learning is used in precise prediction and data-driven redefinition of diseases.


Asunto(s)
Inteligencia Artificial , Neoplasias , Humanos , Aprendizaje Automático , Neoplasias/diagnóstico , Neoplasias/terapia , Medicina de Precisión
12.
Sci Rep ; 10(1): 18926, 2020 11 03.
Artículo en Inglés | MEDLINE | ID: mdl-33144676

RESUMEN

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.


Asunto(s)
Infecciones por Coronavirus/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Aprendizaje Automático , Neumonía Viral/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adulto , Anciano , Infecciones por Coronavirus/patología , Femenino , Humanos , Pulmón/patología , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/patología
13.
Artículo en Inglés | MEDLINE | ID: mdl-33198392

RESUMEN

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.


Asunto(s)
Infecciones por Coronavirus/mortalidad , Aprendizaje Automático , Neumonía Viral/mortalidad , Betacoronavirus , Árboles de Decisión , Humanos , Pandemias , España/epidemiología
14.
Eur Respir Rev ; 29(157)2020 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-33004526

RESUMEN

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.


Asunto(s)
Algoritmos , Inteligencia Artificial , Betacoronavirus , Infecciones por Coronavirus/diagnóstico , Prestación de Atención de Salud/métodos , Aprendizaje Automático , Neumonía Viral/diagnóstico , Neumología/métodos , Humanos , Pandemias
15.
Oecologia ; 194(1-2): 283-298, 2020 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-33006076

RESUMEN

Information on ecological systems often comes from diverse sources with varied levels of complexity, bias, and uncertainty. Accordingly, analytical techniques continue to evolve that address these challenges to reveal the characteristics of ecological systems and inform conservation actions. We applied multiple statistical learning algorithms (i.e., machine learning) with a range of information sources including fish tracking data, environmental data, and visual surveys to identify potential spawning aggregation sites for a marine fish species, permit (Trachinotus falcatus), in the Florida Keys. Recognizing the potential complementarity and some level of uncertainty in each information source, we applied supervised (classic and conditional random forests; RF) and unsupervised (fuzzy k-means; FKM) algorithms. The two RF models had similar predictive performance, but generated different predictor variable importance structures and spawning site predictions. Unsupervised clustering using FKM identified unique site groupings that were similar to the likely spawning sites identified with RF. The conservation of aggregate spawning fish species depends heavily on the protection of key spawning sites; many of these potential sites were identified here for permit in the Florida Keys, which consisted of relatively deep-water natural and artificial reefs with high mean permit residency periods. The application of multiple machine learning algorithms enabled the integration of diverse information sources to develop models of an ecological system. Faced with increasingly complex and diverse data sources, ecologists, and conservation practitioners should find increasing value in machine learning algorithms, which we discuss here and provide resources to increase accessibility.


Asunto(s)
Ecosistema , Aprendizaje Automático , Algoritmos , Animales , Florida , Reproducción
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1234-1237, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018210

RESUMEN

Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions. Machine learning based automated solutions are being developed to reduce the burden of routine screening on Radiologists, allowing them to focus on critical cases. While recent efforts demonstrate the use of ensemble of deep convolutional neural networks (CNN), they do not take disease comorbidity into consideration, thus lowering their screening performance. To address this issue, we propose a Graph Neural Network (GNN) based solution to obtain ensemble predictions which models the dependencies between different diseases. A comprehensive evaluation of the proposed method demonstrated its potential by improving the performance over standard ensembling technique across a wide range of ensemble constructions. The best performance was achieved using the GNN ensemble of DenseNet121 with an average AUC of 0.821 across thirteen disease comorbidities.


Asunto(s)
Aprendizaje Automático , Redes Neurales de la Computación , Comorbilidad , Radiografía , Investigación
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1250-1253, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018214

RESUMEN

Early prediction of cancer response to neoadjuvant chemotherapy (NAC) could permit personalized treatment adjustments for patients, which would improve treatment outcomes and patient survival. For the first time, the efficiency of quantitative computed tomography (qCT) textural and second derivative of textural (SDT) features were investigated and compared in this study. It was demonstrated that intra-tumour heterogeneity can be probed through these biomarkers and used as chemotherapy tumour response predictors in breast cancer patients prior to the start of treatment. These features were used to develop a machine learning approach which provided promising results with cross-validated AUC0.632+, accuracy, sensitivity and specificity of 0.86, 81%, 74% and 88%, respectively.Clinical Relevance- The results obtained in this study demonstrate the potential of textural CT biomarkers as response predictors of standard NAC before treatment initiation.


Asunto(s)
Neoplasias de la Mama , Biomarcadores , Mama , Neoplasias de la Mama/diagnóstico por imagen , Humanos , Aprendizaje Automático , Tomografía Computarizada por Rayos X
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1266-1269, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018218

RESUMEN

Over the last decade, convolutional neural networks (CNNs) have emerged as the leading algorithms in image classification and segmentation. Recent publication of large medical imaging databases have accelerated their use in the biomedical arena. While training data for photograph classification benefits from aggressive geometric augmentation, medical diagnosis - especially in chest radiographs - depends more strongly on feature location. Diagnosis classification results may be artificially enhanced by reliance on radiographic annotations. This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation (similarity transform parameters) of the chest and segmentation of radiographic annotations. Chest x-rays were obtained from published databases. The algorithm was trained with 1000 manually labeled images with augmentation. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2% (n = 500), compared to 27.0% and 34.9% respectively in control images (n = 241). We hypothesize that this pre-processing step will improve robustness in future diagnostic algorithms.Clinical relevance-This work demonstrates a universal pre-processing step for chest radiographs - both normalizing geometry and masking radiographic annotations - for use prior to further analysis.


Asunto(s)
Algoritmos , Redes Neurales de la Computación , Aprendizaje Automático , Radiografía , Rayos X
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1323-1326, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018232

RESUMEN

Despite recent advances in cancer treatment, the prognosis of patients diagnosed with brain metastasis is still poor. The median survival is limited to months even for patients undergoing treatment. Radiation therapy is a main component of treatment for brain metastasis. However, radiotherapy cannot control local progression in up to 20% of the metastatic brain tumours. An early prediction of radiotherapy outcome for individual patients could facilitate therapy adjustments to improve its efficacy. This study investigated the potential of quantitative CT biomarkers in conjunction with machine learning methods to predict local failure after radiotherapy in brain metastasis. Volumetric CT images were acquired for radiation treatment planning from 120 patients undergoing stereotactic radiotherapy. Quantitative features characterizing the morphology and texture were extracted from different regions of each lesion. A feature reduction/selection framework was adapted to define a quantitative CT biomarker of radiotherapy outcome. Different machine learning methods were applied and evaluated to predict the local failure outcome at pre-treatment. The optimum biomarker consisting of two features in conjunction with an AdaBoost with decision tree could predict the local failure outcome with 71% accuracy on an independent test set (20 patients, 31 lesions). This study is a step forward towards prediction of radiotherapy outcome in brain metastasis using quantitative imaging and machine learning.


Asunto(s)
Neoplasias Encefálicas , Radiocirugia , Encéfalo , Neoplasias Encefálicas/diagnóstico por imagen , Humanos , Aprendizaje Automático , Pronóstico , Radiocirugia/efectos adversos
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1339-1342, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018236

RESUMEN

Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.


Asunto(s)
Neoplasias Colorrectales , Neoplasias Hepáticas , Neoplasias Colorrectales/diagnóstico por imagen , Humanos , Neoplasias Hepáticas/diagnóstico por imagen , Aprendizaje Automático , Sensibilidad y Especificidad , Tomografía Computarizada por Rayos X
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