Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer

Lu, Li; Ji, Shuqi; Jiang, Jing; Yoshida, Yu

doi:10.1155/2022/3321014

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The evolving development in big data techniques makes data mining from published database a feasible and affordable way for clinical studies [ 7 , 8 ], although the inconsistency in study design and retrospectively collected data make a lot of parameters incomparable between different studies [ 9 ]. Ideally, comprehensive parameters in the field of psychology and social-economy need to be obtained for a better understanding of factors influencing the prognosis of cervical cancer.…”

Section: Discussionmentioning

confidence: 99%

Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer

Jiang

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. To identify and validate effective clinical predictors for the long-term prognosis of patients with cervical cancer. Methods. Cervical cancer patients were retrieved from the TCGA database, and patients’ clinical data were collected and analyzed for the predictive value of long-term prognosis. In the other branch of the study, patients with cervical cancer and admitted to our hospital between January 1, 2016, and December 31, 2016, were retrieved and followed up for prognosis analysis. Results. In the database patient cohort of our study, 607 cases with cervical cancer were analyzed. Aneuploidy score ( p = 0.012 ), Buffa hypoxia score ( p = 0.013 ), histologic grade ( p = 0.01 ), fraction genome altered >0.4 ( p < 0.001 ), weight > 60 kg ( p < 0.001 ), height > 160 cm ( p = 0.047 ), BMI <18.5 ( p = 0.023 ), Winter hypoxia score ( p = 0.002 ), and adjuvant postoperative radiotherapy were good predictors for disease-free survival (DFS), while aneuploidy score ( p = 0.001 ), MSI sensor score > 0.5 ( p = 0.035 ), person neoplasm status ( p < 0.001 ), race ( p = 0.006 ), Ragnum hypoxia score ( p = 0.012 ), weight ( p < 0.001 ), height ( p < 0.001 ), and BMI < 18.5 ( p = 0.04 ) were good predictors for overall survival (OS). In the admitted patient cohort, age over 60 years old at the time of diagnosis was the only clinical factor influencing the long-term DFS ( p = 0.004 ). TNM stage above III ( p = 0.004 ), body weight > 70 kg ( p < 0.001 ), and complicated with other cancer ( p < 0.001 ) were clinical factor influencing the long-term OS. Conclusions. Clinical factors, especially common to both cohorts, could be used to show the long-term prognosis of cervical cancer.

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Section: Discussionmentioning

confidence: 99%

Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer

Jiang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Es la principal causa de muerte por cáncer ginecológico en Estados Unidos y la quinta causa más común de mortalidad por cáncer en el sexo femenino (3). En Venezuela según las proyecciones de Globocan del año 2020 se estimó una tasa de incidencia 6,1 casos por cada 100 000 mujeres y una tasa de mortalidad de 3,8 casos por cada 100 000 mujeres.…”

Section: Introductionunclassified

“…El tratamiento estándar del cáncer epitelial de ovario avanzado es la cirugía citorreductora primaria en combinación con quimioterapia a base de platinos, siendo el objetivo principal de la cirugía la resección de toda enfermedad macroscópica visible, lo cual se traduce en un aumento de la supervivencia global cuando se compara con la citorreducción subóptima. En contraste, cuando la cirugía citorreductora primaria no es factible debido a estatus performance mayor a 2 puntos, enfermedad en hemiabdomen superior irresecable se recomienda la administración de quimioterapia neoadyuvante 3 a 4 ciclos a base de platinos y paclitaxel, con el propósito de disminuir la carga tumoral y posteriormente realizar cirugía de intervalo (3).…”

Section: Introductionunclassified

Cirugía de intervalo retardada en cáncer epitelial de ovario avanzado

Martínez

2024

Gac Méd Caracas

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Objetivo: Analizar el impacto de la cirugía de intervalo retardada en las pacientes con cáncer epitelial de ovario avanzado que asistieron al Servicio de Ginecología Oncológica del Servicio Oncológico Hospitalario del IVSS. Métodos: Estudio retrospectivo, observacional de corte transversal en pacientes con cáncer epitelial de ovario avanzado. Resultados: Se incluyeron 17 pacientes, el tiempo promedio de retardo de la cirugía fue de 10 semanas (mínimo 4 a máximo 35 semanas). La edad promedio fue de 52,9±9,5 años, en promedio se aplicaron 6 ciclos de quimioterapia. El estadio del cáncer de ovario más frecuente fue el IIIC (88,2 %), seguido del IVA con 11,8 %.

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Survival prediction of ovarian serous carcinoma based on machine learning combined with pathological images and clinical information

Zhou,

Zhao,

Ding

et al. 2024

AIP Advances

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Ovarian serous carcinoma (OSC) has high mortality, making accurate prognostic evaluation vital for treatment selection. This study develops a three-year OSC survival prediction model using machine learning, integrating pathological image features with clinical data. First, a Convolutional Neural Network (CNN) was used to classify the unlabeled pathological images and determine whether they are OSC. Then, we proposed a multi-scale CNN combined with transformer model to extract features directly. The pathological image features were selected by Elastic-Net and then combined with clinical information. Survival prediction is performed using Support Vector Machine (SVM), Random Forest (RF), and XGBoost through cross-validation. For comparison, we segmented the tumor area as the region of interest (ROI) by U-net and used the same methods for survival prediction. The results indicated that (1) the CNN-based cancer classification yielded satisfactory results; (2) in survival prediction, the RF model demonstrated the best performance, followed by SVC, and XGBoost was less effective; (3) the segmented tumor ROIs are more accurate than those predicted directly from the original pathology images; and (4) predictions combining pathological images with clinical information were superior to those solely based on pathological image features. This research provides a foundation for the diagnosis of OSC and individualized treatment, affirming that both ROI extraction and clinical information inclusion enhance the accuracy of predictions.

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Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer

Cited by 3 publications

References 12 publications

Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer

Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer

Cirugía de intervalo retardada en cáncer epitelial de ovario avanzado

Survival prediction of ovarian serous carcinoma based on machine learning combined with pathological images and clinical information

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