RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients

Vezzoli, Marika; Ravaggi, Antonella; Zanotti, Laura; Miscioscia, Rebecca; Bignotti, Eliana; Ragnoli, Monica; Gambino, Angela; Ruggeri, Giuseppina; Calza, Stefano; Sartori, Enrico; Odicino, Franco

doi:10.1038/s41598-017-11104-4

Cited by 18 publications

(16 citation statements)

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“…In the present investigation, we demonstrated for the first time that high sHE4 levels are correlated with aggressive tumor characteristics and worse prognosis in UCC patients. Clinical evidence has confirmed the role of sHE4 as a biomarker with diagnostic and prognostic value in gynecological malignancies, mostly in ovarian and endometrial carcinoma ( 10 – 15 ). In particular, high sHE4 levels have been correlated with poor clinical outcome, as an independent prognostic factor.…”

Section: Discussionmentioning

confidence: 87%

“…Among recently reported serum biomarkers in gynecological cancer, one of the most promising is the Human Epididymis Protein 4 (HE4 or WFDC2), a member of the whey acidic protein (WAP) four-disulfide core gene cluster bearing a conserved motif found in several protease inhibitors ( 8 ). HE4 was first described by Kirchhoff et al ( 9 ) in human epididymal tissue and was subsequently found expressed in many other normal tissues, particularly of the reproductive tracts and of the central respiratory airways, as well as in gynecological malignancies, where HE4 has probably shown the most promising clinical results for early diagnosis and prognosis ( 10 – 15 ). Conversely, data regarding its role in UCC are lacking.…”

Section: Introductionmentioning

confidence: 99%

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Pre-treatment Serum HE4 Level as a Novel Independent Prognostic Biomarker for Uterine Cervical Carcinoma Patients

et al. 2020

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In spite of the effective implementation of screening programs, uterine cervical carcinoma (UCC) remains one of the major causes of cancer death among women around the world. The aim of this study was to investigate the prognostic value of serum human epididymis protein 4 (HE4) in UCC. Pre-treatment serum samples from 109 UCC patients and 99 healthy women were analyzed for HE4 levels by a quantitative chemiluminescent microparticle immunoassay on the automated ARCHITECT instrument. HE4 serum (sHE4) levels were significantly higher in UCC patients, regardless of tumor stage, compared with healthy controls. Elevated sHE4 levels were significantly associated with advanced FIGO stage and absence of disease-free interval after treatment. In univariable analysis, higher sHE4 levels were significantly correlated with shorter overall survival and progression-free survival. In multivariable analysis, sHE4 retained its significance as independent adverse prognostic factor for both survival endpoints. This study indicates that sHE4 is associated with a more aggressive tumor phenotype and a worse patient's prognosis. These results suggest the potential role of sHE4 as a novel prognostic marker and as an indicator of high-risk UCC patients for a tailored surgical and adjuvant therapy.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Pre-treatment Serum HE4 Level as a Novel Independent Prognostic Biomarker for Uterine Cervical Carcinoma Patients

et al. 2020

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“…https://thejns.org/doi/abs/10.3171/2020.9.FOCUS20681 KEYWORDS pandemic; anxiety; machine learning Three different models were used to identify which variables (X) have the greatest impact on the outcomes (Y). Because the variables were of a different nature (qualitative and quantitative), mostly asymmetrical, and related to Y by nonlinear relationships, a machine learning approach was chosen: the random forest (RF), 24 which is an ensemble method [25][26][27] that combines thousands of regression trees. 28 The idea behind the RF is quite simple: first, the algorithm perturbs the data set, generating many bootstrap samples and growing, on each of them, an overfitted decision tree (i.e., each terminal node contains few observations).…”

Section: (In Detail Between Stai-y1 [State] and Stai-y2 [Trait] Scormentioning

confidence: 99%

Anxiety in neurosurgical patients undergoing nonurgent surgery during the COVID-19 pandemic

Doglietto

Vezzoli

Biroli

et al. 2020

Neurosurgical Focus

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OBJECTIVEThe COVID-19 pandemic has forced many countries into lockdown and has led to the postponement of nonurgent neurosurgical procedures. Although stress has been investigated during this pandemic, there are no reports on anxiety in neurosurgical patients undergoing nonurgent surgical procedures.METHODSNeurosurgical patients admitted to hospitals in eastern Lombardy for nonurgent surgery after the lockdown prospectively completed a pre- and postoperative structured questionnaire. Recorded data included demographics, pathology, time on surgical waiting list, anxiety related to COVID-19, primary pathology and surgery, safety perception during hospital admission before and after surgery, and surgical outcomes. Anxiety was measured with the State-Trait Anxiety Inventory. Descriptive statistics were computed on the different variables and data were stratified according to pathology (oncological vs nononcological). Three different models were used to investigate which variables had the greatest impact on anxiety, oncological patients, and safety perception, respectively. Because the variables (Xs) were of a different nature (qualitative and quantitative), mostly asymmetrical, and related to outcome (Y) by nonlinear relationships, a machine learning approach composed of three steps (1, random forest growing; 2, relative variable importance measure; and 3, partial dependence plots) was chosen.RESULTSOne hundred twenty-three patients from 10 different hospitals were included in the study. None of the patients developed COVID-19 after surgery. State and trait anxiety were reported by 30.3% and 18.9% of patients, respectively. Higher values of state anxiety were documented in oncological compared to nononcological patients (46.7% vs 25%; p = 0.055). Anxiety was strongly associated with worry about primary pathology, surgery, disease worsening, and with stress during waiting time, as expected. Worry about positivity to SARS-CoV-2, however, was the strongest factor associated with anxiety, even though none of the patients were infected. Neuro-oncological disease was associated with state anxiety and with worry about surgery and COVID-19. Increased bed distance and availability of hand sanitizer were associated with a feeling of safety.CONCLUSIONSThese data underline the importance of psychological support, especially for neuro-oncological patients, during a pandemic.

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“…To estimate the BS-EWM, the outcome (Alive/Dead) was modeled using as covariates: (i) Brescia X-ray score, (ii) 17 analytes, (iii) age. Since most of the covariates analyzed were strongly correlated (multicollinearity) (Figure S1) and their relationships with the outcome were non-linear, the BS-EWM was estimated using Random Forests (Breiman, 2001) (Carpita and Vezzoli, 2012), a non-parametric machine-learning method (Vezzoli, 2011) (Vezzoli et al, 2017). Moreover, the algorithm is able to manage missing values which are common in clinical studies.…”

Section: Methodsmentioning

confidence: 99%

Early Prediction of In-Hospital Death of COVID-19 Patients: A Machine-Learning Model Based on Age, Blood Analyses, and Chest X-Ray Score

Garrafa

Vezzoli

Ravanelli

et al. 2021

Preprint

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BackgroundTo develop and validate an early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED).MethodsIn total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital.ResultsThe final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively.ConclusionsThe model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

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RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients

Cited by 18 publications

References 41 publications

Pre-treatment Serum HE4 Level as a Novel Independent Prognostic Biomarker for Uterine Cervical Carcinoma Patients

Pre-treatment Serum HE4 Level as a Novel Independent Prognostic Biomarker for Uterine Cervical Carcinoma Patients

Anxiety in neurosurgical patients undergoing nonurgent surgery during the COVID-19 pandemic

Early Prediction of In-Hospital Death of COVID-19 Patients: A Machine-Learning Model Based on Age, Blood Analyses, and Chest X-Ray Score

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