Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy.

OBJECTIVE: The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). METHODS: Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A "radiomic model" was created with only radiomic features, and a "patho-radiomic model" was created using radiomic features and immunohistochemical data. RESULTS: Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model's sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903). CONCLUSIONS: Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.

Una perspectiva diferente sobre la radiómica con 18F-FDG-PET en pacientes con cáncer colorrectal; la relación entre el análisis intra y peritumoral y los hallazgos patológicos / A different perspective on18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findings

Objetivo El objetivo de nuestro estudio fue determinar el valor de la tomografía por emisión de positrones/tomografía computarizada con 18F-fluorodesoxiglucosa (PET/TC con18F-FDG) basada en la radiómica del tumor primario y peritumoral en la predicción de depósitos tumorales (TD), crecimiento tumoral (TB) e invasión venosa extramural (EMVI) del cáncer colorrectal (CCR). Métodos Nuestro estudio retrospectivo incluyó a 77 pacientes con CCR a los que se les realizó un 18F-FDG PET/TC preoperatoria entre junio de 2020 y febrero de 2022. Se extrajeron un total de 131 características radiómicas del tumor primario y áreas peritumorales en imágenes de fusión PET/TC. Se investigó la relación entre TD, TB, EMVI y estadio T en el estudio patológico postoperatoria de los tumores y las características radiómicas. Las características con un coeficiente de correlación (CC) inferior a 0,8 se analizaron con regresión logística. El rendimiento del modelo se evaluó mediante el área bajo la curva (AUC) obtenida del análisis de las características operativas del receptor. Resultados Se desarrolló un modelo a partir de datos de radiómica peritumoral y tumor primario para predecir el estadio T (AUC 0,931), y también se construyó un modelo predictivo a partir de radiómica derivada del tumor primario para predecir EMVI (AUC 0,739). Los datos radiómicos derivados del tumor primario se obtuvieron como factor pronóstico predictivo del DT y se encontró que una característica peritumoral era un factor pronóstico en la predicción de TB. Conclusiones La radiómica intratumoral y peritumoral derivada de la PET con18F-FDG es útil para la predicción preoperatoria no invasiva de propiedades patológicas que tienen implicaciones importantes en el manejo del CCR (AU)

Objective We aimed to determine the value of 18F-fluorodeoxyglucose positron emission tomogra-phy/computed tomography (18F-FDG PET/CT) based primary tumoral and peritumoral radiomics in the prediction of tumor deposits (TDs), tumor budding (TB) and extramural venous invasion (EMVI) of colorectal cancer (CRC). Methods Our retrospective study included 77 CRC patients who had preoperative18F-FDG PET/CT between June 2020 and February 2022. A total of 131 radiomic features were extracted from primary tumors and peritumoral areas on PET/CT fusion images. The relationship between TDs, TB, EMVI and T stage in the postoperative pathology of the tumors and radiomic features was investigated. Features with a correlation coefficient (CC) less than 0.8 were analyzed by logistic regression. The area under curve (AUC) obtained from the receiver operating characteristic analysis was used to measure the model performance. Results A model was developed from primary tumoral and peritumoral radiomics data to predict T stage (AUC 0.931), and also a predictive model was constructed from primary tumor derived radiomics to predict EMVI (AUC 0.739). Radiomic data derived from the primary tumor was obtained as a predictive prognostic factor in predicting TDs and a peritumoral feature was found to be a prognostic factor in predicting TB. Conclusions Intratumoral and peritumoral radiomics derived from18F-FDG PET/CT are useful for non-invasive early prediction of pathological features that have important implications in the management of CRC (AU)

Heterogeneidad del tumor primario en la18F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras terapia neoadyuvante / Primary tumor heterogeneity on pretreatment18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy

Objetivo Estudio retrospectivo cuyo objetivo fue investigar el valor de las características de textura de los tumores primarios en la PET/TC con 18F-FDG pretratamiento para la predicción de la respuesta al tratamiento, la progresión y la supervivencia global en pacientes con cáncer de recto que se sometieron a cirugía después de la terapia neoadyuvante (TNA). Métodos Se incluyeron en este estudio pacientes con cáncer de recto que se sometieron a estudio PET/TC con 18F-FDG antes del tratamiento y se sometieron a cirugía después de TNA. Se registraron las características clínico-patológicas, la fecha del último seguimiento, la evolución y fallecimiento. Los parámetros de las texturas y los convencionales de PET (Standard Uptake Value-SUVmax, volumen tumoral metabólico-MTV, glucólisis total de la lesión-TLG) se obtuvieron a partir de imágenes PET/TC utilizando el programa LifeX. Los parámetros se agruparon utilizando el índice de Youden en el análisis ROC. Los factores que predicen la respuesta patológica al tratamiento, la progresión y la supervivencia global se determinaron mediante regresión logística y análisis de regresión de Cox. Resultados Cuarenta y cuatro pacientes (26-59% hombres, 18-41% mujeres; 60,1 ± 11,4 años) con cáncer de recto fueron incluidos en este estudio. El número de pacientes respondedores y no respondedores a TNA fueron de 15 (34,9%) y 28 (65,1%), respectivamente. La mediana de la duración del seguimiento fue de 29,9 meses. 9 (20,5%) mostraron progresión de la enfermedad y 8 (18,2%) fallecieron durante el período de seguimiento. Los parámetros de entropía GLCM de diferencia y correlación GLCM se encontraron como predictores independientes para la respuesta a TNA. Los parámetros de positividad del margen quirúrgico, rango intercuartílico de intensidad CONV y textura AUC-CSHDISC fueron predictores independientes de progresión (AU)

Purpose This retrospective study aimed to investigate the value of texture features of primary tumors in pretreatment18F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after neoadjuvant therapy (NAT). Method Patients with rectal cancer who had pretreatment18F-FDG PET/CT, and underwent surgery after NAT were included in this study. Clinicopathologic features, date of last follow-up, progression, and death were recorded. Textural and conventional PET parameters (maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis-TLG) were obtained from PET/CT images using LifeX program. Parameters were grouped using Youden index in ROC analysis. Factors predicting the pathological response to treatment, progression, and overall survival were determined using logistic regression and Cox regression analyses. Results Forty-four patients (26(59%) male, 18 (41%) female; 60.1 ± 11.4 years) with rectal cancer were included in this study. The numbers of patients with responders and non-responders to NAT were15(34.9%) and 28(65.1%), respectively. One patient’ pathology report did not contain the response status to NAT. The median of follow-up duration was 29.9 months. 9(20.5%) showed disease progression, and 8(18.2%) died during the follow-up period. Difference entropy GLCM and correlation GLCM parameters were found as independent predictors for response to NAT. The positivity of surgical margin, intensity interquartile range CONV and AUC-CSHDISC texture parameters were independent predictors of progression, while normalized inverse difference GLCM and LZLGEGLZLM parameters were independent predictorsof mortality. Conclusion The texture parameters obtained from pretreatment18F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer who underwent surgery after NAT (AU)

Explorando la opinión de los especialistas españoles acerca de la utilidad de la radiómica en el área oncológica / Exploring the opinion of Spanish medical specialists about the usefulness of radiomics in oncology

Objetivo Describir el conocimiento y la opinión de los profesionales sanitarios relacionados con la oncología acerca de la radiómica. Métodos Se elaboró un cuestionario de 12 preguntas (respuestas de selección múltiple, de escala tipo Likert y respuesta abierta), dirigido a profesionales relacionados con el diagnóstico/tratamiento de enfermedades oncológicas (oncología, radiodiagnóstico, medicina nuclear, oncología-radioterápica, hematooncología, radiofísica y anatomía patológica). Los participantes se clasificaron en dos grupos según su grado de formación: adjuntos y residentes. Resultados Un total de 114 profesionales completaron la encuesta (54% residentes, principalmente de las especialidades medicina nuclear y radiodiagnóstico). Los adjuntos obtuvieron un mejor desempeño en el área de conocimiento respecto a los residentes. En ambos grupos los encuestados respondieron estar de acuerdo con la utilidad de la radiómica para ayudar a realizar diagnósticos más precisos, facilitando el trabajo de los equipos médicos. Las ideas más frecuentes relacionadas con las desventajas del uso de la radiómica se relacionaron con la falta de sistematización en la adquisición de imágenes y la extracción de parámetros, la necesidad de entrenamiento de los profesionales y la inquietud sobre el reemplazo del trabajo humano por herramientas tecnológicas. Conclusiones La radiómica es un campo de estudio novedoso, cuyos aspectos más generales son conocidos por los profesionales sanitarios. Los profesionales encuestados son optimistas en cuanto a los beneficios que entregan esta y otros tipos de herramientas. El principal problema detectado fue la falta de sistematización en su implementación. El reemplazo de los profesionales y la pérdida de trabajo es una preocupación presente, pero menos prevalente y que puede responder a un fenómeno generacional (AU)

Aim To describe the knowledge and opinion of health professionals regarding the usefulness of radiomics in oncology. Methods A 12-question questionnaire (multiple-choice responses, Likert-type scale, and open response) was developed and sent to professionals related to diagnosis/treatment of oncological diseases (Oncology, Radiodiagnosis, Nuclear Medicine, Radiation Oncology, Hematology-Oncology, Radiophysics and Pathology). Participants were classified into two groups according to their level of training: attending physicians and residents. Results 114 professionals completed the survey (54% residents, mostly from Nuclear Medicine and Radiodiagnostic specialties). Attending physicians obtained a better performance in the area pf knowledge compared to residents. Both groups of respondents agreed regarding the usefulness of radiomics to help make more accurate diagnoses and promoting the work of medical teams and the most frequent disadvantages were related to the lack of systematization in the acquisition of images and extraction of parameters, the need for the training of professionals and concern about the replacement of human work by technological tools. Conclusions Radiomics is a novel field and the most general aspects are known by health professionals. The professionals surveyed were optimistic about the benefits provided by radiomics and other types of tools. The main problem detected was the lack of systematization in its implementation. The replacement of professionals and job loss is a concern, albeit less prevalent, and may respond to a generational phenomenon (AU)

A different perspective on 18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findings.

OBJECTIVE: We aimed to determine the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) based primary tumoral and peritumoral radiomics in the prediction of tumor deposits (TDs), tumor budding (TB) and extramural venous invasion (EMVI) of colorectal cancer (CRC). METHODS: Our retrospective study included 77 CRC patients who had preoperative 18F-FDG PET/CT between June 2020 and February 2022. A total of 131 radiomic features were extracted from primary tumors and peritumoral areas on PET/CT fusion images. The relationship between TDs, TB, EMVI and T stage in the postoperative pathology of the tumors and radiomic features was investigated. Features with a correlation coefficient (CC) less than 0.8 were analyzed by logistic regression. The area under curve (AUC) obtained from the receiver operating characteristic analysis was used to measure the model performance. RESULTS: A model was developed from primary tumoral and peritumoral radiomics data to predict T stage (AUC 0.931), and also a predictive model was constructed from primary tumor derived radiomics to predict EMVI (AUC 0.739). Radiomic data derived from the primary tumor was obtained as a predictive prognostic factor in predicting TDs and a peritumoral feature was found to be a prognostic factor in predicting TB. CONCLUSIONS: Intratumoral and peritumoral radiomics derived from 18F-FDG PET/CT are useful for non-invasive early prediction of pathological features that have important implications in the management of CRC.

Progreso y utilidad actual de la radiómica dentro del estudio PET/TC en cáncer de mama no metastásico: una revisión sistemática / Progress and current utility of radiomics in PET/CT study of non-metastatic breast cancer: A systematic review

Objetivo Sintetizar la evidencia actual sobre la utilidad de la radiómica en el análisis de la imagen PET/TC en cáncer de mama local o localmente avanzado y evaluar la calidad metodológica de los estudios radiómicos publicados al respecto. Material y métodos Revisión sistemática de artículos en distintas bases de datos hasta 2021 utilizando los términos «PET», «radiomics», «texture», «breast». Se seleccionaron solo artículos con datos humanos y que incluyeran una imagen de PET en su análisis. Se excluyeron estudios con datos de pruebas y menos de 20 pacientes. De cada artículo se extrajo el tamaño muestral, el radiotrazador utilizado, la técnica de imagen y las características de imagen extraídas. Se determinó la calidad metodológica de los estudios mediante el instrumento QUADAS-2. Resultados Se seleccionaron 18 artículos. El diseño retrospectivo fue el más utilizado. La característica radiómica más estudiada fue el SUVmax. Diversos parámetros radiómicos se correlacionaron con la caracterización tumoral, y la heterogeneidad tumoral demostró utilidad para predecir el curso de la enfermedad y la respuesta al tratamiento. La mayoría de los artículos mostraron un alto riesgo de sesgo, derivado principalmente de la selección de pacientes. Conclusiones Se observó una alta probabilidad de sesgo en los artículos publicados. La radiómica es un campo aún en desarrollo y son necesarios más estudios para demostrar su utilidad en la práctica clínica habitual. La herramienta QUADAS-2 permite la valoración crítica de la calidad metodológica de la evidencia disponible. Pese a las limitaciones, la radiómica se muestra como una herramienta que puede ayudar a conseguir un manejo oncológico personalizado en el cáncer de mama (AU)

Aim To synthesize the current evidence of the usefulness of radiomics in PET/CT image analysis in local and locally advanced breast cancer. Also, to evaluate the methodological quality of the radiomic studies published. Methods Systematic review of articles in different databases until 2021 using the terms «PET», «radiomics», «texture», «breast». Only articles with human data and that included a PET image were included. Studies with simulated data and with less than 20 patients were excluded. The sample size, radiotracer used, imaging technique, and radiomics characteristics were extracted from each article. The methodological quality of the studies was determined using the QUADAS-2 tool. Results Eighteen articles were selected. The retrospective design was the most used. The most studied radiomic characteristic was SUVmax. Several radiomic parameters were correlated with tumor characterization, and tumor heterogeneity proved useful for predicting disease course and response to treatment. Most articles showed a high risk of bias, mainly from the patient selection. Conclusions A high probability of bias was observed in most of the published articles. Radiomics is a developing field and more studies are needed to demonstrate its usefulness in routine clinical practice. The QUADAS-2 tool allows critical assessment of the methodological quality of the available evidence. Despite its limitations, radiomics is shown to be an instrument that can help to achieve personalized oncologic management of breast cancer (AU)

Exploring the opinion of Spanish medical specialists about the usefulness of radiomics in oncology.

AIM: To describe the knowledge and opinion of health professionals regarding the usefulness of radiomics in oncology. METHODS: A 12-question questionnaire (multiple-choice responses, Likert-type scale, and open response) was developed and sent to professionals related to diagnosis/treatment of oncological diseases (Oncology, Radiodiagnosis, Nuclear Medicine, Radiation Oncology, Hematology-Oncology, Radiophysics and Pathology). Participants were classified into two groups according to their level of training: attending physicians and residents. RESULTS: 114 professionals completed the survey (54% residents, mostly from Nuclear Medicine and Radiodiagnostic specialties). Attending physicians obtained a better performance in the area pf knowledge compared to residents. Both groups of respondents agreed regarding the usefulness of radiomics to help make more accurate diagnoses and promoting the work of medical teams and the most frequent disadvantages were related to the lack of systematization in the acquisition of images and extraction of parameters, the need for the training of professionals and concern about the replacement of human work by technological tools. CONCLUSIONS: Radiomics is a novel field and the most general aspects are known by health professionals. The professionals surveyed were optimistic about the benefits provided by radiomics and other types of tools. The main problem detected was the lack of systematization in its implementation. The replacement of professionals and job loss is a concern, albeit less prevalent, and may respond to a generational phenomenon.

Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy.

PURPOSE: This retrospective study aimed to investigate the value of texture features of primary tumors in pretreatment 18F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after neoadjuvant therapy(NAT). METHODS: Patients with rectal cancer who had pretreatment 18F-FDG PET/CT, and underwent surgery after NAT were included in this study. Clinicopathologic features, date of last follow-up, progression, and death were recorded. Textural and conventional PET parameters(maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis-TLG) were obtained from PET/CT images using LifeX program. Parameters were grouped using Youden index in ROC analysis. Factors predicting the pathological response to treatment, progression, and overall survival were determined using logistic regression and Cox regression analyses. RESULTS: Forty-four patients (26(59%) male, 18(41%) female; 60.1±11.4 years) with rectal cancer were included in this study. The numbers of patients with responders and non-responders to NAT were 15(34.9%) and 28(65.1%), respectively. One patient' pathology report did not contain the response status to NAT. The median of follow-up duration was 29.9 months. 9(20.5%) showed disease progression, and 8(18.2%) died during the follow-up period. Difference entropyGLCM and correlationGLCM parameters were found as independent predictors for response to NAT. The positivity of surgical margin, intensity interquartile rangeCONV and AUC-CSHDISC texture parameters were independent predictors of progression, while normalized inverse differenceGLCM and LZLGEGLZLM parameters were independent predictors of mortality. CONCLUSION: The texture parameters obtained from pretreatment 18F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer who underwent surgery after NAT.

Progress and current utility of radiomics in PET/CT study of non-metastatic breast cancer: A systematic review.

AIM: To synthesize the current evidence of the usefulness of radiomics in PET/CT image analysis in local and locally advanced breast cancer. Also, to evaluate the methodological quality of the radiomic studies published. METHODS: Systematic review of articles in different databases until 2021 using the terms "PET", "radiomics", "texture", "breast". Only articles with human data and that included a PET image were included. Studies with simulated data and with less than 20 patients were excluded. Were extracted sample size, radiotracer used, imaging technique, and radiomics characteristics from each article. The methodological quality of the studies was determined using the QUADAS-2 tool. RESULTS: 18 articles were selected. The retrospective design was the most used. The most studied radiomic characteristic was SUVmax. Several radiomic parameters were correlated with tumor characterization, and tumor heterogeneity proved useful for predicting disease course and response to treatment. Most articles showed a high risk of bias, mainly from the patient selection. CONCLUSIONS: A high probability of bias was observed in most of the published articles. Radiomics is a developing field and more studies are needed to demonstrate its usefulness in routine clinical practice. The QUADAS-2 tool allows critical assessment of the methodological quality of the available evidence. Despite its limitations, radiomics is shown to be an instrument that can help to achieve personalized oncologic management of breast cancer.

Magnetic resonance imaging texture analysis of the temporomandibular joint for changes in the articular disc in individuals with migraine headache / Análise de textura por ressonância magnética da articulação temporomanbibular com alterações no disco articular em individuos com cefaleia migranea

Objective: the aim of this study was to analyse the performance of the technique of texture analysis (TA) with magnetic resonance imaging (MRI) scans of temporomandibular joints (TMJs) as a tool for identification of possible changes in individuals with migraine headache (MH) by relating the findings to the presence of internal derangements. Material and Methods: thirty MRI scans of the TMJ were selected for study, of which 15 were from individuals without MH or any other type of headache (control group) and 15 from those diagnosed with migraine. T2-weighted MRI scans of the articular joints taken in closed-mouth position were used for TA. The co-occurrence matrix was used to calculate the texture parameters. Fisher's exact test was used to compare the groups for gender, disc function and disc position, whereas Mann-Whitney's test was used for other parameters. The relationship of TA with disc position and function was assessed by using logistic regression adjusted for side and group. Results: the results indicated that the MRI texture analysis of articular discs in individuals with migraine headache has the potential to determine the behaviour of disc derangements, in which high values of contrast, low values of entropy and their correlation can correspond to displacements and tendency for non-reduction of the disc in these individuals. Conclusion: the TA of articular discs in individuals with MH has the potential to determine the behaviour of disc derangements based on high values of contrast and low values of entropy (AU)

Objetivo: o objetivo deste estudo foi analisar o desempenho da técnica de análise de textura (AT) em exames de ressonância magnética (RM) das articulações temporomandibulares (ATM) como ferramenta para identificação de possíveis alterações em indivíduos com cefaléia migrânea (CM) relacionando os achados com a presença de desarranjos internos. Material e Métodos: trinta exames de RM das ATM foram selecionados para estudo, sendo 15 de indivíduos sem cefaleia migrânea ou qualquer outro tipo de cefaléia (grupo controle) e 15 diagnosticados com CM. As imagens de RM ponderadas em T2 das articulações realizadas na posição de boca fechada foram usadas para AT. A matriz de co-ocorrência foi usada para calcular os parâmetros de textura. O teste exato de Fisher foi usado para comparar os grupos quanto ao sexo, função do disco e posição do disco, enquanto o teste de Mann-Whitney foi usado para os demais parâmetros. A relação da AT com a posição e função do disco foi avaliada por meio de regressão logística ajustada para lado e grupo. Resultados: a AT por RM dos discos articulares em indivíduos com cefaleia migrânea tem o potencial de determinar o comportamento dos desarranjos discais, em que altos valores de contraste, baixos valores de entropia e sua correlação podem corresponder a deslocamentos e tendência a não redução do disco nesses indivíduos. Conclusão: a análise de textura dos discos articulares em indivíduos com CM tem potencial para determinar o comportamento dos desarranjos do disco com base em altos valores de contraste e baixos valores de entropia. (AU)

Detección de fibrosis en imágenes de cine de resonancia cardíaca mediante el uso de técnicas de inteligencia artificial / Fibrosis detection on MR-Cine images by using artificial intelligence techniques

RESUMEN Introducción: Las técnicas de inteligencia artificial han demostrado tener un gran potencial en el área de la cardiología, especialmente para identificar patrones imperceptibles para el ser humano. En este sentido, dichas técnicas parecen ser las adecuadas para identificar patrones en la textura del miocardio con el objetivo de identificar y cuantificar la fibrosis. Objetivos: Proponer un nuevo método de inteligencia artificial para identificar fibrosis en imágenes cine de resonancia cardíaca. Materiales y métodos: Se realizó un estudio retrospectivo observacional en 75 sujetos del Sanatorio San Carlos de Bariloche. El método propuesto analiza la textura del miocardio en las imágenes cine CMR (resonancia magnética cardíaca) mediante el uso de una red neuronal convolucional que determinar el daño local del tejido miocárdico. Resultados: Se observó una precisión del 89% para cuantificar el daño tisular local en el conjunto de datos de validación y de un 70% para el conjunto de prueba. Además, el análisis cualitativo realizado muestra una alta correlación espacial en la localización de la lesión. Conclusiones: El método propuesto permite identificar espacialmente la fibrosis únicamente utilizando la información de los estudios de cine de resonancia magnética nuclear, mostrando el potencial de la técnica propuesta para cuantificar la viabilidad miocárdica en un futuro o estudiar la etiología de las lesiones.

ABSTRACT Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the etiology of lesions.

La radiómica mejora la utilidad del ADC en la diferenciación entre el oncocitoma renal y el carcinoma cromófobo de células renales: resultados preliminares / Radiomics improves the utility of ADC for differentiation between renal oncocytoma and chromophobe renal cell carcinoma: preliminary findings

Objective Differentiation between renal oncocytoma (RON) and chromophobe renal cell carcinoma (chRCC) remains challenging. We aimed to assess the accurate apparent diffusion coefficient (ADC) radiomics features in differentiating these tumors.Materials and methods This single-center retrospective study included 14 patients with histopathologically proven RON (n=6) and chRCC (n=8) who underwent magnetic resonance imaging. Features were extracted from ADC maps. Features with an intraclass correlation coefficient >0.90, an intergroup p<0.01 and interrater differences with normal distribution underwent agreement and receiver operating characteristic curve analyses.Results Overall, 6 features qualified for further analysis and Bland-Altman plots revealed acceptable agreement for all. Only 1 first order feature and 5 high order texture features successfully predicted RON with more than 90% sensitivities and specificities more than 80%.Conclusion Squared mean ADC and certain gray level run length matrix features extracted by radiomics of ADC mapping provide quite high diagnostic precision in terms of distinguishing between RON and chRCC (AU)

Objetivo La diferenciación entre el oncocitoma renal (OR) y el carcinoma de células renales cromófobo (CCRcr) sigue siendo un desafío. Nuestro objetivo es evaluar la precisión de las características radiómicas del coeficiente de difusión aparente (ADC) para diferenciar estos tumores.Materiales y métodos Este estudio retrospectivo unicéntrico incluyó a 14 pacientes con OR (n = 6) y CCRcr (n = 8) confirmado por informe histológico que recibieron una resonancia magnética. Las características se extrajeron de los mapas de ADC. Las características con un coeficiente de correlación intraclase > 0,90, un p < 0,01 intergrupo y diferencias interevaluadores con distribución normal se sometieron a análisis de concordancia y de curva característica de funcionamiento del receptor.Resultados En total, se obtuvieron seis características para el análisis posterior y los gráficos de Bland-Altman revelaron una concordancia aceptable para todas ellas. Sólo una característica de primer orden y cinco características de textura de orden superior predijeron con éxito el OR con una sensibilidad superior al 90% y una especificidad superior al 80%.Conclusión La media cuadrada del ADC y ciertas características de la matriz de longitud de secuencia de nivel de gris extraídas por la radiómica del mapa de ADC proporcionan una precisión diagnóstica bastante alta en cuanto a la distinción entre OR y CCRcr (AU)

Radiomics improves the utility of ADC for differentiation between renal oncocytoma and chromophobe renal cell carcinoma: Preliminary findings.

OBJECTIVE: Differentiation between renal oncocytoma (RON) and chromophobe renal cell carcinoma (chRCC) remains challenging. We aimed to assess the accurate apparent diffusion coefficient (ADC) radiomics features in differentiating these tumors. MATERIALS AND METHODS: This single-center retrospective study included 14 patients with histopathologically proven RON (n = 6) and chRCC (n = 8) who underwent magnetic resonance imaging. Features were extracted from ADC maps. Features with an intraclass correlation coefficient >0.90, an intergroup p < 0.01 and interrater differences with normal distribution underwent agreement and receiver operating characteristic curve analyses. RESULTS: Overall, 6 features qualified for further analysis and Bland-Altman plots revealed acceptable agreement for all. Only 1 first order feature and 5 high order texture features successfully predicted RON with more than 90% sensitivities and specificities more than 80%. CONCLUSION: Squared mean ADC and certain gray level run length matrix features extracted by radiomics of ADC mapping provide quite high diagnostic precision in terms of distinguishing between RON and chRCC.

El valor de los datos y su aplicabilidad en el Sector Sanitario / The value of data and its applicability in the Health Sector

Actualmente las noticias y/o artículos sobre la utilización de la Inteligencia Artificial (IA) y el Big Data nos están inundando y esta situación se ha agudizado con la pandemia, donde se ha dado una gran importancia a su utilización y las diversas aplicaciones en todos los sectores. Unos ámbitos tecnológicos y de oportunidades que cada vez se encuentran más presentes en nuestro día a día. El sector que más crecimiento ha experimento durante este tiempo de pandemia es, sin lugar a dudas, el sector sanitario. La imperiosa necesidad ha fomentado y agilizado el uso de estas tecnologías. La utilización de datos para poder acometer tratamientos en un breve tiempo, ver las evoluciones de las diferentes enfermedades y predecir su estado es lo que ha impulsado su utilización y donde debido a la situación cualquier ayuda era y es poca. Desde este artículo pretendemos dar una explicación de los beneficios del uso de la IA y las diferentes técnicas del Big Data, tanto en el estudio y evolución de enfermedades como en su prevención, detección, seguimiento y tratamiento (AU)

Currently news and/or articles on the use of Artificial Intelligence and the Big Data are flooding us and this situation has worsened with the pandemic, where great importance has been given to its use and the various applications in all sectors. Some areas of technology and opportunities that are increasingly are more present in our day to day. The sector that has experienced the most growth during this time of pandemic is, without a doubt, the Health sector. The imperative need has fostered and expedited the use of these technologies. The use of data to be able to undertake treatments in a short time, see the evolutions of the different diseases and predict their state is what has driven its use and where due to the situation any help was and is little. From this article we intend to give an explanation of the benefits of using the Artificial Intelligence and the different Big Data techniques, both in the study and evolution of diseases as in their prevention, detection, monitoring and treatment (AU)

The value of data and its applicability in the Health Sector.

Currently news and/or articles on the use of Artificial Intelligence and the Big Data are flooding us and this situation has worsened with the pandemic, where great importance has been given to its use and the various applications in all sectors. Some areas of technology and opportunities that are increasingly are more present in our day to day. The sector that has experienced the most growth during this time of pandemic is, without a doubt, the Health sector. The imperative need has fostered and expedited the use of these technologies. The use of data to be able to undertake treatments in a short time, see the evolutions of the different diseases and predict their state is what has driven its use and where due to the situation any help was and is little. From this article we intend to give an explanation of the benefits of using the Artificial Intelligence and the different Big Data techniques, both in the study and evolution of diseases as in their prevention, detection, monitoring and treatment.

Interdisciplinarity: An essential requirement for translation of radiomics research into clinical practice -a systematic review focused on thoracic oncology. / Interdisciplinaridad: un requerimiento esencial para la traslación de investigación en radiómica a la práctica clínica.

BACKGROUND: Recently, evidence has accumulated that demonstrates the potential for future applications of radiomics in many clinical settings, including thoracic oncology. Methodological reasons for the immaturity of image mining (radiomics and artificial intelligence-based) studies have been identified. However, data on the influence of the composition of the research team on the quality of investigations in radiomics are lacking. AIM: This review aims to evaluate the interdisciplinarity within studies on radiomics in thoracic oncology in order to assess its influence on the quality of research (QUADAS-2 score) in the image mining field. METHODS: We considered for inclusion radiomics investigations with objectives relating to clinical practice in thoracic oncology. Subsequently, we interviewed the corresponding authors. The field of expertise and/or educational degree was then used to assess interdisciplinarity. Subsequently, all studies were evaluated applying the QUADAS-2 score and assigned to a research phase from 0 to IV. RESULTS: Overall, 27 studies were included. The study quality according to the QUADAS-2 score was low (score ≤5) in 8, moderate (=6) in 12, and high (≥7) in 7 papers. An interdisciplinary team (at least 3 different expertise categories) was involved in half of the papers without any type of validation and in all papers with independent validation. Clinicians were not involved in phase 0 studies while they contributed to all papers classified as phase I and to 4/5 papers classified as phase II with independent validation. CONCLUSIONS: The composition of the research team influences the quality of investigations in radiomics. Also, growth in interdisciplinarity appears to reflect research development from the early phase to a more mature, clinically oriented stage of investigation.

Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine / Inteligência artificial, aprendizado de máquina, diagnóstico auxiliado por computador e radiômica: avanços da imagem rumo à medicina de precisão

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to "know everything about all exams and regions". In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, "big data", and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

Resumo A disciplina de radiologia e diagnóstico por imagem evoluiu sobremaneira nos últimos anos. Temos observado o aumento exponencial do número de exames realizados, a subespecialização das disciplinas médicas e a maior acurácia dos métodos, tornando um desafio para o médico radiologista "saber tudo sobre todos exames e regiões". Além disso, os exames de imagem deixaram de ser somente qualitativos e diagnósticos e passaram a fornecer informações quantitativas e de gravidade de doença, identificando biomarcadores prognósticos e de resposta ao tratamento. Diante disso, sistemas computadorizados de auxílio diagnóstico vêm sendo desenvolvidos com o objetivo dar suporte ao diagnóstico por imagem e à decisão terapêutica. Com o advento da inteligência artificial, do big data e do aprendizado de máquina, caminhamos para a rápida expansão do uso dessas ferramentas no dia-a-dia dos médicos, tornando cada paciente único, levando a radiologia ao encontro do conceito de abordagem multidisciplinar e medicina de precisão. Neste artigo serão abordados os principais aspectos das ferramentas computacionais atualmente disponíveis para análise das imagens médicas, apresentando os princípios de análise das imagens, os principais termos e conceitos envolvidos nesses processos, assim como o impacto do desenvolvimento da inteligência artificial na radiologia e diagnóstico por imagem.