Risks of Muscle Atrophy in Patients with Malignant Lymphoma after Autologous Stem Cell Transplantation

Hirota, Kaoru; Matsuse, Hiroo; Koya, Shunji; Hashida, Ryuki; Bekki, Masafumi; Yanaga, Yoko; Johzaki, Kiyoko; Tomino, Mami; Mouri, Fumihiko; Morishige, Satoshi; Oya, Shuki; Yamasaki, Yoshitaka; Nagafuji, Koji; Shiba, Naoto

doi:10.1298/ptr.e10041

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Convolutional layers: The convolutional layer is the basic building block of CNN models, and it bears the largest burden in network computations. In this layer, two matrices are multiplied: one of them is the filter f (t), which is one of the basic parameters in the convolutional layer; the second matrix is in the part from the image x (t), which is the same size as the filter as in Equation (1). The size of the filter determines how much it wraps around the image [28].…”

Section: Deep Feature Extractionmentioning

confidence: 99%

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Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

et al. 2022

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Malignant lymphoma is one of the types of malignant tumors that can lead to death. The diagnostic method for identifying malignant lymphoma is a histopathological analysis of lymphoma tissue images. Because of the similar morphological characteristics of the lymphoma types, it is difficult for doctors and specialists to manually distinguish the types of lymphomas. Therefore, deep and automated learning techniques aim to solve this problem and help clinicians reconsider their diagnostic decisions. Because of the similarity of the morphological characteristics between lymphoma types, this study aimed to extract features using various algorithms and deep learning models and combine them together into feature vectors. Two datasets have been applied, each with two different systems for the reliable diagnosis of malignant lymphoma. The first system was a hybrid system between DenseNet-121 and ResNet-50 to extract deep features and reduce their dimensions by the principal component analysis (PCA) method, using the support vector machine (SVM) algorithm for classifying low-dimensional deep features. The second system was based on extracting the features using DenseNet-121 and ResNet-50 and combining them with the hand-crafted features extracted by gray level co-occurrence matrix (GLCM), fuzzy color histogram (FCH), discrete wavelet transform (DWT), and local binary pattern (LBP) algorithms and classifying them using a feed-forward neural network (FFNN) classifier. All systems achieved superior results in diagnosing the two datasets of malignant lymphomas. An FFNN classifier with features of ResNet-50 and hand-crafted features reached an accuracy of 99.5%, specificity of 100%, sensitivity of 99.33%, and AUC of 99.86% for the first dataset. In contrast, the same technique reached 100% for all measures to diagnose the second dataset.

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Section: Deep Feature Extractionmentioning

confidence: 99%

“…Malignant lymphoma is considered to be one of the most dangerous and complex cancers that lead to death. The exposure of lymphocytes to malignant lesions leads to the formation of malignant tumors [1]. Lymphoma accounts for 50% of malignant blood diseases [2].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

et al. 2022

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“…Las características propias de estas enfermedades y los efectos secundarios que generan sus tratamientos, provocan un descenso de la condición física y psicológica, que conlleva unos peores resultados en salud (Celis-Morales, et al, 2017;Del Castillo, Hernández, Luján & Olivares, 2021). Asimismo, pacientes que reciben tratamientos como la quimioterapia o autotrasplantes de células madre que conllevan largos periodos de hospitalización, sufren un descenso de la masa y la fuerza muscular (Davis & Panikkar, 2019;Hirota, et al, 2021;Persoon et al, 2017). Como consecuencia, estos perciben un descenso de la función física y la calidad de vida, así como, debilidad, sensación de fatiga, ansiedad y depresión entre otros (Campbell, et al, 2019).…”

Section: Introductionunclassified

Condición física y calidad de vida de personas con patologías crónicas graves: programa de ejercicio EfiKroniK (Physical fitness and quality of life of patients with severe chronic diseases: EfiKroniK exercise program)

Domínguez-Martínez,

Arietaleanizbeaskoa,

Gil Rey

et al. 2023

Retos

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El objetivo del estudio es describir las características clínicas, de condición física y calidad de vida de una serie de casos compuesta por 162 pacientes crónicos diagnosticados de patologías hematológicas, trastorno mental o cáncer sólido en estadio IV, incluidos en el ensayo clínico EfiKroniK. Se recogieron datos sociodemográficos y clínicos además de realizar diferentes test para medir la condición física (test 400 metros, test de prensión manual y test de sentarse y levantarse cinco veces) y la calidad de vida general (SF-36, Golberg y Duke) y específica (PANSS y EORT QLQ-C30). Al analizar los resultados encontramos que tanto la condición física (262 segundos en el test de los 400 m, Intervalo de Confianza al 95% [IC95%] = 253 – 271; y 34kg en el test de prensión manual, IC95%=13 – 15) como la calidad de vida (41 puntos en el componente resumen de salud física del SF-36; IC95% = 39 – 42) son netamente inferiores a las de la población general, con diferencias significativas (p<0,05) entre grupos de patología. Las personas con enfermedades crónicas presentan un deterioro en su condición física y calidad de vida que deben tenerse en cuenta para prescribirles planes de ejercicio físico (CEIC de Euskadi, PI2014127). Palabras clave: Condición física; fuerza; capacidad cardiorrespiratoria; calidad de vida; enfermedades crónicas; cáncer estadío IV; trastornos mentales severos; cáncer hematológico Abstract. The aim of the study is to describe the clinical, physical fitness and quality of life characteristics of a case series of 162 patients diagnosed with a haematological pathology, a mental disorder or stage IV solid cancer, included in the EfiKroniK clinical trial. Sociodemographic and clinical data were collected and different tests were performed to measure physical fitness (400-metre test, handgrip test and sit-to-stand test five times) and general (SF-36, Golberg and Duke) and specific (PANSS and EORT QLQ-C30) quality of life. Analysing the results we found that both physical fitness (262 seconds in the 400m test, 95% Confidence Interval [95%CI] = 253 - 271; and 34kg in the handgrip test, 95%CI = 13 - 15) and quality of life (41 points in the physical health summary component of the SF-36; 95%CI = 39 - 42) are markedly lower than in the general population, with significant differences (p<0.05) between pathology groups. People with chronic diseases present a deterioration in their physical condition and quality of life that should be taken into account when prescribing physical exercise plans (CEIC de Euskadi, PI2014127). Key words: Physical fitness; strength; cardiorespiratory fitness; quality of live; chronic diseases; stage IV cancer; severe mental disorders; hematological cancer

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Prognostic Role of "Radiological" Sarcopenia in Lymphoma: A Systematic Review

Albano

Dondi

Ravanelli

et al. 2022

Clinical Lymphoma Myeloma and Leukemia

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Risks of Muscle Atrophy in Patients with Malignant Lymphoma after Autologous Stem Cell Transplantation

Cited by 5 publications

References 39 publications

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

Condición física y calidad de vida de personas con patologías crónicas graves: programa de ejercicio EfiKroniK (Physical fitness and quality of life of patients with severe chronic diseases: EfiKroniK exercise program)

Prognostic Role of "Radiological" Sarcopenia in Lymphoma: A Systematic Review

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