Dataset Information

Nomogram for Preoperative Estimation of Orbit Invasion Risk in Periocular Squamous Cell Carcinoma.

ABSTRACT: Importance: Orbital invasion occurs in some periocular squamous cell carcinoma (SCC), compromising surgical outcomes, and prognoses of patients. To date, however, there are no validation studies on the clinical features related to orbital invasion in patients with periocular SCC. Objective: To explore clinical features that may be associated with orbital invasion and build a model for predicting the risk of orbital invasion. Design, Setting, and Participants: In this retrospective mono-center case-control study, 90 patients with periocular SCC were treated at the Ninth People's Hospital Shanghai Jiao Tong University School of Medicine from January 2005 to August 2019. "Case" is defined as a SCC patient with orbit invasion prior to operation. "Exposure" is defined as the different sites of lesion. Main Outcomes and Measures: Clinical features, including "time to relapse after surgery," were collected. Multivariate logistic regression analysis was applied to identify the independent risk clinical features associated with orbital invasion, which was then incorporated into a nomogram. Results: Of the 90 patients included in this study, 33 patients (36.7%) had orbital invasion. 14 of the 33 orbit-invasive patients had local recurrence, while 11 of 57 orbit non-invasive patients had local recurrence, suggesting that orbital invasion is a risk factor for local recurrence. The multivariate binary logistic regression indicated that the lesions at the medial canthus [odds ratio (OR), 5.024, 95% CI, 1.409-17.912, P = 0.013], the age at diagnosis (10-years intervals; OR, 0.590, 95% CI, 0.412-0.844, P = 0.004), and bleeding in the lesion (OR, 3.480, 95% CI, 1.254-9.660, P = 0.017) were three preoperative clinical features significantly associated with orbital invasion. Conclusion: For periocular SCC, lesions at the medial canthus, the younger age of the patients at diagnosis, and bleeding in the lesion were the three main clinical features associated with orbital invasion. The risk score model for orbital invasion can act as a supportive tool for optimized clinical evaluation and treatment decisions.

SUBMITTER: Xie M

PROVIDER: S-EPMC7203342 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Nomogram for Preoperative Estimation of Orbit Invasion Risk in Periocular Squamous Cell Carcinoma.

Xie Minyue M Yu Jie J Li Lunhao L Jia Renbing R Song Xin X Wang Yefei Y Fan Xianqun X

Frontiers in oncology 20200430

Importance: Orbital invasion occurs in some periocular squamous cell carcinoma (SCC), compromising surgical outcomes, and prognoses of patients. To date, however, there are no validation studies on the clinical features related to orbital invasion in patients with periocular SCC. Objective: To explore clinical features that may be associated with orbital invasion and build a model for predicting the risk of orbital invasion. Design, Setting, and Participants: In this retrosp ...[more]

PMID: 32426276

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Project description:The lymphovascular invasion (LVI) status facilitates the determination of the optimal therapeutic strategy for superficial esophageal squamous cell carcinoma (SESCC), but in clinical practice, LVI must be confirmed by postoperative pathology. However, studies of the risk factors for LVI in SESCC are limited. Consequently, this study aimed to identify the risk factors for LVI and use these factors to establish a prediction model. The data of 516 patients who underwent radical esophagectomy between January 2007 and September 2019 were retrospectively collected (training set, n=361, January 2007 to May 2015; validation set, n=155, June 2015 to September 2019). In the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were utilized to identify predictive factors for LVI in patients with SESCC. A nomogram was then developed using these predictors. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the efficiency, accuracy, and clinical utility of the model. LASSO regression indicated that the tumor size, depth of invasion, tumor differentiation, lymph node metastasis (LNM), sex, circumferential extension, the presence of multiple lesions, and the resection margin were correlated with LVI. However, multivariate analysis revealed that only the tumor size, depth of invasion, tumor differentiation, and LNM were independent risk factors for LVI. Incorporating these four variables, model 1 achieved an AUC of 0.817 in predicting LVI. Adding circumferential extension to model 1 did not appreciably change the AUC and integrated discrimination improvement, but led to a significant increase in the net reclassification improvement (p=0.011). A final nomogram was constructed by incorporating tumor size, depth of invasion, tumor differentiation, LNM, and circumferential extension and showed good discrimination (training set, AUC=0.833; validation set, AUC=0.819) and good calibration in the training and validation sets. Decision curve analysis demonstrated that the nomogram was clinically useful in both sets. Thus, it is possible to predict the status of LVI using this nomogram scoring system, which can aid the selection of an appropriate treatment plan.

Project description:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Project description:INTRODUCTION:Extracapsular extension (ECE) in regional lymph nodes and positive surgical margins (PSM) are considered high-risk adverse pathologic features in patients with oropharyngeal squamous cell carcinoma (OPSCC) that each constitute an indication for postoperative adjuvant chemoradiation. We identify pre-operative clinical factors that can predict post-operative ECE and/or PSM and create a nomogram to help clinical decision making. METHODS:Adult patients with non-metastatic OPSCC with initial surgical treatment and confirmed HPV status diagnosed between 2010 and 2014 were selected from the National Cancer Database. Clinical staging was modified to American Joint Committee on Cancer 8th edition parameters. Logistic regression was used for multivariate analysis to identify predictors of pathologic ECE and/or PSM. RESULTS:5065 patients were included. 47.5% of the 3336 HPV-positive (HPV+) patients had ECE/PSM. 40.4% of the 1729 HPV-negative (HPV-) patients with had ECE/PSM. A model was built that included age, clinical ECE, tumor grade, and clinical T and N staging for HPV+ patients. Increasing N-classification was highly predictive of pathologic ECE and/or PSM (N1 OR?=?3.6, N2 OR?=?7.0, N3 OR?=?11.2, p?<?0.01). Clinical ECE (OR?=?4.1, p?<?0.01), tumor grade (ORs 2.2-4.4 with p?<?0.05), and increasing clinical T-classification (ORs 1.2-1.8, p?<?0.05) were also associated with ECE and/or PSM. A similar model was built for HPV- with similar predictive capability. Two internally validated nomograms were designed that demonstrated good discrimination (HPV+ AUC?=?0.66, 95% CI: 0.64-0.68, and HPV- AUC?=?0.70, 95% CI: 0.67-0.72) and good calibration (goodness-of-fit statistic of HPV+ 6.32, p?=?0.61 and HPV- 11.66, p?=?0.17). CONCLUSIONS:These are the first nomograms designed to help predict ECE or PSM for both HPV+ and HPV- OPSCC. The nomograms can facilitate shared decision-making between clinicians and patients as they consider upfront treatment selection for OPSCC.

Dataset Information

Nomogram for Preoperative Estimation of Orbit Invasion Risk in Periocular Squamous Cell Carcinoma.

Publications

Nomogram for Preoperative Estimation of Orbit Invasion Risk in Periocular Squamous Cell Carcinoma.

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