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  • MDPI AG  (5)
  • Lee, Maria  (5)
  • 1
    Online Resource
    Online Resource
    Machine Learning Models to Predict Survival Outcomes According to the Surgical Approach of Primary Radical Hysterectomy in Patients with Early Cervical Cancer
    MDPI AG ; 2021
    In:  Cancers Vol. 13, No. 15 ( 2021-07-23), p. 3709-
    Details
    In: Cancers, MDPI AG, Vol. 13, No. 15 ( 2021-07-23), p. 3709-
    Abstract: We purposed to develop machine learning models predicting survival outcomes according to the surgical approach for radical hysterectomy (RH) in early cervical cancer. In total, 1056 patients with 2009 FIGO stage IB cervical cancer who underwent primary type C RH by either open or laparoscopic surgery were included in this multicenter retrospective study. The whole dataset consisting of patients’ clinicopathologic data was split into training and test sets with a 4:1 ratio. Using the training set, we developed models predicting the probability of 5-year progression-free survival (PFS) and overall survival (OS) with tenfold cross validation. The developed models were validated in the test set. In terms of predictive performance, we measured the area under the receiver operating characteristic curve (AUC) values. The logistic regression models comprised of preoperative variables yielded AUCs of 0.679 and 0.715 for predicting 5-year PFS and OS rates, respectively. Combining both logistic regression and multiple machine learning models, we constructed hybrid ensemble models, and these models showed much improved predictive performance, with 0.741 and 0.759 AUCs for predicting 5-year PFS and OS rates, respectively. We successfully developed models predicting disease recurrence and mortality after primary RH in patients with early cervical cancer. As the predicted value is calculated based on the preoperative factors, such as the surgical approach, these ensemble models would be useful for making decisions when choosing between open or laparoscopic RH.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers13153709
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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  • 2
    Online Resource
    Online Resource
    Impact of Adjuvant Radiotherapy on Survival Outcomes in Intermediate-Risk, Early-Stage Cervical Cancer: Analyses Regarding Surgical Approach of Radical Hysterectomy
    MDPI AG ; 2020
    In:  Journal of Clinical Medicine Vol. 9, No. 11 ( 2020-11-03), p. 3545-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 9, No. 11 ( 2020-11-03), p. 3545-
    Abstract: This study aimed to investigate the impact of adjuvant radiotherapy (RT) on survival outcomes in patients with intermediate-risk, early-stage cervical cancer who underwent radical hysterectomy (RH). From the cervical cancer cohorts of two tertiary hospitals, patients with 2009 FIGO stage IB-IIA who underwent primary RH between 2010 and 2018 were identified. Patients with intermediate-risk factors that met the Sedlis criteria were included. Survival outcomes were compared between the patients who received adjuvant RT (study group; n = 53) and those who did not receive adjuvant treatment (control group; n = 30). Compared to the control group, the study group showed significantly better recurrence-free survival (RFS; 5-year survival rate, 85.6% vs. 61.0%; p = 0.009). In multivariate analysis, adjuvant RT was associated with a significantly lower risk of disease recurrence (adjusted HR, 0.241; 95% CI, 0.082–0.709; p = 0.010). In a subgroup that underwent open RH (n = 33), adjuvant RT showed a trend toward improved RFS with borderline statistical significance (adjusted HR, 0.098; 95% CI, 0.009–1.027; p = 0.053). However, in a subgroup of minimally invasive surgery (n = 50), adjuvant RT did not improve RFS. In conclusion, implementation of adjuvant RT significantly reduced the disease recurrence rate in patients with intermediate-risk, stage IB-IIA cervical cancer treated primarily with surgery. Survival benefit from adjuvant RT differed according to the surgical approach.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm9113545
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662592-1
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  • 3
    Online Resource
    Online Resource
    Metagenomic Analysis of Serum Microbe-Derived Extracellular Vesicles and Diagnostic Models to Differentiate Ovarian Cancer and Benign Ovarian Tumor
    MDPI AG ; 2020
    In:  Cancers Vol. 12, No. 5 ( 2020-05-21), p. 1309-
    Details
    In: Cancers, MDPI AG, Vol. 12, No. 5 ( 2020-05-21), p. 1309-
    Abstract: We aimed to develop a diagnostic model identifying ovarian cancer (OC) from benign ovarian tumors using metagenomic data from serum microbe-derived extracellular vesicles (EVs). We obtained serum samples from 166 patients with pathologically confirmed OC and 76 patients with benign ovarian tumors. For model construction and validation, samples were randomly divided into training and test sets in the ratio 2:1. Isolation of microbial EVs from serum samples of the patients and 16S rDNA amplicon sequencing were carried out. Metagenomic and clinicopathologic data-based OC diagnostic models were constructed in the training set and then validated in the test set. There were significant differences in the metagenomic profiles between the OC and benign ovarian tumor groups; specifically, genus Acinetobacter was significantly more abundant in the OC group. More importantly, Acinetobacter was the only common genus identified by seven different statistical analysis methods. Among the various metagenomic and clinicopathologic data-based OC diagnostic models, the model consisting of age, serum CA-125 levels, and relative abundance of Acinetobacter showed the best diagnostic performance with the area under the receiver operating characteristic curve of 0.898 and 0.846 in the training and test sets, respectively. Thus, our findings establish a metagenomic analysis of serum microbe-derived EVs as a potential tool for the diagnosis of OC.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers12051309
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527080-1
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  • 4
    Online Resource
    Online Resource
    Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma
    MDPI AG ; 2020
    In:  Cancers Vol. 12, No. 4 ( 2020-03-26), p. 790-
    Details
    In: Cancers, MDPI AG, Vol. 12, No. 4 ( 2020-03-26), p. 790-
    Abstract: Initial identification of biomarkers predicting the exact prognosis of high-grade serous ovarian carcinoma (HGSOC) is important in precision cancer medicine. This study aimed to investigate prognostic biomarkers of HGSOC through proteomic analysis. We conducted label-free liquid chromatography-mass spectrometry using chemotherapy-naïve, fresh-frozen primary HGSOC specimens, and compared the results between a favorable prognosis group (progression-free survival (PFS) ≥ 18 months, n = 6) and a poor prognosis group (PFS 〈 18 months, n = 6). Among 658 differentially expressed proteins, 288 proteins were upregulated in the favorable prognosis group and 370 proteins were upregulated in the poor prognosis group. Using hierarchical clustering, we selected α1-antitrypsin (AAT), nuclear factor-κB (NFKB), phosphomevalonate kinase (PMVK), vascular adhesion protein 1 (VAP1), fatty acid-binding protein 4 (FABP4), platelet factor 4 (PF4), apolipoprotein A1 (APOA1), and α1-acid glycoprotein (AGP) for further validation via immunohistochemical (IHC) staining in an independent set of chemotherapy-naïve primary HGSOC samples (n = 107). Survival analyses revealed that high expression of AAT, NFKB, and PMVK were independent biomarkers for favorable PFS. Conversely, high expression of VAP1, FABP4, and PF4 were identified as independent biomarkers for poor PFS. Furthermore, we constructed models predicting the 18-month PFS by combining clinical variables and IHC results. Through leave-one-out cross-validation, the optimal model was based on initial serum CA-125, germline BRCA1/2 mutations, residual tumors after surgery, International Federation of Gynecology and Obstetrics (FIGO) stage, and expression levels of the six proteins. The present results elucidate the proteomic landscape of HGSOC and six protein biomarkers to predict the prognosis of HGSOC.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers12040790
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527080-1
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  • 5
    Online Resource
    Online Resource
    Lower Extremity Lymphedema in Gynecologic Cancer Patients: Propensity Score Matching Analysis of External Beam Radiation versus Brachytherapy
    MDPI AG ; 2019
    In:  Cancers Vol. 11, No. 10 ( 2019-09-30), p. 1471-
    Details
    In: Cancers, MDPI AG, Vol. 11, No. 10 ( 2019-09-30), p. 1471-
    Abstract: The goal of this study is to compare the risk of lower extremity lymphedema (LEL) between pelvic external beam radiation therapy (EBRT) and vaginal brachytherapy, and to identify risk factors for LEL in gynecologic cancer patients treated with adjuvant radiation therapy (RT) after radical surgery. A total of 263 stage I–III gynecologic cancer patients who underwent adjuvant RT were retrospectively reviewed. One-to-one case-matched analysis was conducted with propensity scores generated from patient, tumor, and treatment characteristics. Using the risk factors found in this study, high- and low-risk groups were identified. With a median follow-up of 36.0 months, 35 of 263 (13.3%) patients developed LEL. In multivariate analysis, laparoscopic surgery (HR 2.548; p = 0.024), harvesting more than 30 pelvic lymph nodes (HR 2.246; p = 0.028), and para-aortic lymph node dissection (PALND, HR 2.305; p = 0.014) were identified as independent risk factors for LEL. After propensity score matching, the LEL incidence of the brachytherapy group was significantly lower than the EBRT group (p = 0.025). In conclusion, high-risk patients with risk factors such as laparoscopic surgery, harvesting more than 30 pelvic lymph nodes, PALND, and adjuvant pelvic EBRT require closer observation for LEL.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers11101471
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2527080-1
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