Preoperative Evaluation of Perineural Invasion in Cervical Cancer: Development and Independent Validation of a Novel Predictive Nomogram

BackgroundPerineural invasion (PNI) is associated with a poor prognosis for cervical cancer and influences surgical strategies. However, a preoperative evaluation that can determine PNI in cervical cancer patients is lacking.MethodsAfter 1:1 propensity score matching, 162 cervical cancer patients with PNI and 162 cervical cancer patients without PNI were included in the training set. Forty-nine eligible patients were enrolled in the validation set. The PNI-positive and PNI-negative groups were compared. Multivariate logistic regression was performed to build the PNI prediction nomogram.ResultsAge [odds ratio (OR), 1.028; 95% confidence interval (CI), 0.999–1.058], adenocarcinoma (OR, 1.169; 95% CI, 0.675–2.028), tumor size (OR, 1.216; 95% CI, 0.927–1.607), neoadjuvant chemotherapy (OR, 0.544; 95% CI, 0.269–1.083), lymph node enlargement (OR, 1.953; 95% CI, 1.086–3.550), deep stromal invasion (OR, 1.639; 95% CI, 0.977–2.742), and full-layer invasion (OR, 5.119; 95% CI, 2.788–9.799) were integrated in the PNI prediction nomogram based on multivariate logistic regression. The PNI prediction nomogram exhibited satisfactory performance, with areas under the curve of 0.763 (95% CI, 0.712–0.815) for the training set and 0.860 (95% CI, 0.758–0.961) for the validation set. Moreover, after reviewing the pathological slides of patients in the validation set, four patients initially diagnosed as PNI-negative were recognized as PNI-positive. All these four patients with false-negative PNI were correctly predicted to be PNI-positive (predicted p > 0.5) by the nomogram, which improved the PNI detection rate.ConclusionThe nomogram has potential to assist clinicians when evaluating the PNI status, reduce misdiagnosis, and optimize surgical strategies for patients with cervical cancer.

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A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer

Frontiers in Pharmacology ◽

10.3389/fphar.2020.585255 ◽

2020 ◽

Vol 11 ◽

Author(s):

Peijie Chen ◽

Yuting Gao ◽

Si Ouyang ◽

Li Wei ◽

Min Zhou ◽

...

Keyword(s):

Cervical Cancer ◽

Cancer Patients ◽

Prognostic Model ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Training Set ◽

Non Coding Rnas ◽

Validation Set ◽

Immune Related Genes

Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups.Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment.Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with longer OS and low-risk group with shorter OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p < 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher.Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.

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A Nomogram Based on Clinical Characteristics for Predicting Multiple Organ Dysfunction Syndrome(MODS) Following Multiple Trauma Patients

10.21203/rs.3.rs-78104/v1 ◽

2020 ◽

Author(s):

ZhenJun Miao ◽

Faxing Wei ◽

Feng Zhou

Keyword(s):

Logistic Regression ◽

Multiple Trauma ◽

Clinical Characteristics ◽

Area Under The Curve ◽

Trauma Patients ◽

Multivariate Logistic Regression ◽

Training Set ◽

Lemeshow Test ◽

Validation Set ◽

Multiple Trauma Patients

Abstract BackgroundMultiple organ dysfunction syndrome (MODS) is the one of common complications,and the leading cause of late mortality in multiple trauma patients.The present study aims to develop and validate a nomogram based on clinical characteristics in order to identify the patients with multiple trauma who were at risk of developing MODS.MethodsAn retrospective cohort study was performed with data from January 2011 to December 2019,totally 770 patients with multiple trauma were enrolled in our study.They were randomly categorized into training set (n=514) and validation set (n=256).The univariate and multivariate logistic regression analyses were used to screen the predictors for multiple trauma patients who were at risk of developing MODS from training set data.Then we established a nomogram based on these above predictors.The discriminative capacity was assessed by receiver operating characteristic (ROC) curve area under the curve (AUC), and the predictive precision was depicted by calibration plot.The Hosmer-Lemeshow test was used to evaluate the the model’s goodness of fit.ResultsOur study showed that age,ISS,hemorrhagic shock,heart rate,blood glucose,D-dimer and APTT were independent risk factors for MODS in patients with multiple trauma by multivariate logistic regression analysis.A nomogram was established on basis of these above risk factors.The area under the curve (AUC) was 0.868 (95% confidence interval [CI]:0.829-0.908) in the training set and 0.884 (95% confidence interval [CI]:0.833-0.935) in the validation set.The Hosmer-Lemeshow test has a p value of 0.227 in training set and 0.554 in validation set respectively,which confirm the model’s goodness of fit.Calibration plot showed that the predicted and actual incidence of MODS probability were fitted well on both internal and external validations.ConclusionsThe present nomogram had a well predictive precision and discrimination capacity,which can facilitate improved screening and early identification of multiple trauma patients who were at high risk of developing MODS.

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Development and Validation of a Nomogram Based on Noninvasive Liver Reserve and Fibrosis (PALBI and FIB-4) Model to Predict Posthepatectomy Liver Failure Grade B-C in Patients with Hepatocellular Carcinoma

Journal of Oncology ◽

10.1155/2021/6665267 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wenhui Zhong ◽

Feng Zhang ◽

Kaijun Huang ◽

Yiping Zou ◽

Yubin Liu

Keyword(s):

Risk Factors ◽

Hepatocellular Carcinoma ◽

Logistic Regression ◽

Liver Failure ◽

Roc Curves ◽

Postoperative Liver Failure ◽

Multivariate Logistic Regression ◽

Training Set ◽

Independent Risk Factors ◽

Validation Set

Hepatectomy is currently one of the most effective treatments for hepatocellular carcinoma (HCC). However, postoperative liver failure (PHLF) is a serious complication and the leading cause of mortality in patients with HCC after hepatectomy. This study attempted to develop a novel nomogram based on noninvasive liver reserve and fibrosis models, platelet-albumin-bilirubin grade (PALBI) and fibrosis-4 index (FIB-4), able to predict PHLF grade B-C. This was a single-centre retrospective study of 574 patients with HCC undergoing hepatectomy between 2014 and 2018. The independent risk factors of PHLF were screened using univariate and multivariate logistic regression analyses. Multivariate logistic regression was performed using the training set, and the nomogram was developed and visualised. The utility of the model was evaluated in a validation set using the receiver operating characteristic (ROC) curve. A total of 574 HCC patients were included (383 in the training set and 191 for the validation set) and included PHLF grade B-C complications of 14.8, 15.4, and 13.6%, respectively. Overall, cirrhosis ( P < 0.026 , OR = 2.296, 95% confidence interval (CI) 1.1.02–4.786), major hepatectomy ( P = 0.031 , OR = 2.211, 95% CI 1.077–4.542), ascites ( P = 0.014 , OR = 3.588, 95% 1.299–9.913), intraoperative blood loss ( P < 0.001 , OR = 4.683, 95% CI 2.281–9.616), PALBI score >−2.53 (, OR = 3.609, 95% CI 1.486–8.764), and FIB-4 score ≥1.45 ( P < 0.001 , OR = 5.267, 95% CI 2.077–13.351) were identified as independent risk factors associated with PHLF grade B-C in the training set. The areas under the ROC curves for the nomogram model in predicting PHLF grade B-C were significant for both the training and validation sets (0.832 vs 0.803). The proposed nomogram predicted PHLF grade B-C among patients with HCC with a better prognostic accuracy than other currently available fibrosis and noninvasive liver reserve models.

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A Nomogram for Predicting the Risk of Common Bile Duct Stones Based on Preoperative Laboratory Tests and Ultrasonography

10.21203/rs.3.rs-265606/v1 ◽

2021 ◽

Author(s):

Kai Wu ◽

Dong Wang ◽

Jiegao Zhu ◽

Kun Liu ◽

Hongwei Wu ◽

...

Keyword(s):

Logistic Regression ◽

Bile Duct ◽

Common Bile Duct ◽

Predictive Factors ◽

Roc Curve ◽

Laboratory Tests ◽

Multivariate Logistic Regression ◽

Training Set ◽

Validation Set ◽

Cbd Stone

Abstract Objective: The objective of this study was to determine the predictive factors for common bile duct (CBD) stone and establish a nomogram model based on the preoperative laboratory tests and imaging findings.Methods: A total of 1701 patients who underwent laparoscopic cholecystectomy (LC) combined with common bile duct exploration (CBDE) for suspected choledochlithiasis from November 2014 to October 2020 were eligible for this analysis. All patients were divided into the training set (from November 2014 to November 2019, n=1,453) and validation set (from November 2019 to October 2020, n=248) based on the admission time. The predictive factors for CBD stone were determined by the univariate and multivariate logistic regression model. A nomogram model for predicting the presence of CBD stone was developed based on significant variables, and receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. Results: The results of multivariate logistic regression analysis demonstrated that multiple gallbladder stones (OR: 7.463, 95%CI: 5.437-10.243, P<0.001), the maximal diameter of CBD stone measured by preoperative ultrasonography (OR for 0.8-1.5 cm: 4.756, 95%CI: 3.513-6.438, P<0.001; OR for 1.5-2.0 cm: 9.597, 95%CI: 4.621-19.931, P<0.001; OR for >2.0 cm: 24.473, 95%CI: 2.809-213.207, P<0.001), preoperative GGT level (OR for 90-225 U/L: 2.828, 95%CI: 1.898-4.214, P<0.001; OR for 225-450 U/L: 9.994, 95%CI: 4.668-21.403, P<0.001; OR for >450 U/L: 12.535, 95%CI: 4.452-35.292, P<0.001) and DB/TB ratio (OR: 394.329, 95%CI: 79.575-1954.064, P<0.001) were independent predictive factors for CBD stone. The nomogram model for predicting the presence of CBD stone was developed based on the above-mentioned variables. ROC curve showed that the C-index of the nomogram model for the training set and validation set was 0.875 (95% CI: 0.857-0.893) and 0.834 (95% CI: 0.784-0.883), which were better than that of MRCP for preoperative diagnosis of CBD stone. The calibration curve and DCA curve demonstrated that the nomogram model had a good clinical utility for predicting the presence of CBD stone .Conclusion: The nomogram based on preoperative laboratory tests and ultrasonography had an excellent predictive power for CBD stone, and it might provide useful information for making treatment strategies.

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Serum Long Non-Coding RNAs PVT1, HOTAIR, and NEAT1 as Potential Biomarkers in Egyptian Women with Breast Cancer

Biomolecules ◽

10.3390/biom11020301 ◽

2021 ◽

Vol 11 (2) ◽

pp. 301

Author(s):

Amal Ahmed Abd El-Fattah ◽

Nermin Abdel Hamid Sadik ◽

Olfat Gamil Shaker ◽

Amal Mohamed Kamal ◽

Nancy Nabil Shahin

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Cancer Patients ◽

Breast Cancer Diagnosis ◽

Serum Levels ◽

Breast Cancer Patients ◽

Multivariate Logistic Regression ◽

Expression Levels ◽

Non Coding Rnas ◽

Pai 1

Long non-coding RNAs play an important role in tumor growth, angiogenesis, and metastasis in several types of cancer. However, the clinical significance of using lncRNAs as biomarkers for breast cancer diagnosis and prognosis is still poorly investigated. In this study, we analyzed the serum expression levels of lncRNAs PVT1, HOTAIR, NEAT1, and MALAT1, and their associated proteins, PAI-1, and OPN, in breast cancer patients compared to fibroadenoma patients and healthy subjects. Using quantitative real-time PCR (qRT-PCR), we compared the serum expression levels of the four circulating lncRNAs in patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The serum levels of PAI-1 and OPN were measured using ELISA. Receiveroperating-characteristic (ROC) analysis and multivariate logistic regression were used to evaluate the diagnostic value of the selected parameters. The serum levels of HOTAIR, PAI-1, and OPN were significantly higher in breast cancer patients compared to controls and fibroadenoma patients. The serum level of PVT1 was significantly higher in breast cancer patients than in the controls, while that of NEAT1 was significantly lower in breast cancer patients compared to controls and fibroadenoma patients. Both ROC and multivariate logistic regression analyses revealed that PAI-1 has the greatest power in discriminating breast cancer from the control, whereas HOTAIR, PAI-1, and OPN have the greatest power in discriminating breast cancer from fibroadenoma patients. In conclusion, our data suggest that the serum levels of PVT1, HOTAIR, NEAT1, PAI-1, and OPN could serve as promising diagnostic biomarkers for breast cancer.

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Evaluation of Subjective Sleep Disturbances in Cancer Patients: A Cross-Sectional Study in a Radiotherapy Department

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.648896 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jun Wang ◽

Bei-Yun Zhou ◽

Chen-Lu Lian ◽

Ping Zhou ◽

Hui-Juan Lin ◽

...

Keyword(s):

Logistic Regression ◽

Sleep Quality ◽

Sleep Disturbance ◽

Sleep Disorders ◽

Cancer Patients ◽

Sleep Disturbances ◽

Cancer Type ◽

Multivariate Logistic Regression ◽

Subjective Sleep ◽

Radiotherapy Department

Background: The factors associated with sleep disturbances in cancer patients remains unclear. This study aimed to explore the prevalence of sleep disorders and predictors associated with sleep disturbance in cancer patients from a radiotherapy department.Methods: Patients with cancers were recruited before the start of radiotherapy from our institution between January 2019 and February 2020. Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. Descriptive statistics, Chi-square test, and multivariate logistic regression analysis were used to conduct statistical analysis.Results: A total of 330 eligible patients were included. Of them, 38.3% (n = 127) had the globe PSQI score >7, indicating that they suffered from sleep disorders. Patients with lung cancer (45.2%) were more likely to suffer from sleep disturbance, followed by cervical cancer (43.8%), nasopharyngeal carcinoma (41.7%), esophageal cancer (41.5%), breast cancer (37.7%), and colorectal cancer (30%). With regard to the PSQI components, the mean sleep duration was 8 h, 20.3% (n = 67) of them reported poor subjective sleep quality, 6.1% (n = 20) needed medication to improve sleep, and 53.6% (n = 177) suffered daytime dysfunction. Multivariate logistic regression models showed body mass index (BMI) ≥ 20 kg/m2 [odds ratio (OR) 0.599, 95% confidence interval (CI) 0.329–0.948, P = 0.031] and the receipt of surgery (OR 0.507, 95% CI 0.258–0.996, P = 0.048) were the significant favorable predictors for sleep disturbance, while age, gender, marital status, education level, comorbidity, metastasis status, diagnostic status, and cancer type were not significantly associated with sleep disturbance.Conclusions: Approximately 40% of the cancer patients suffer from sleep disturbance before the start of radiotherapy. Patients with BMI ≥ 20 kg/m2 and receiving surgery are less likely to develop sleep disturbance in comparison with others.

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Conventional pap’s test and liquid-based cytology for the screening of cervical cancer with back up colposcopy directed biopsy

Bangabandhu Sheikh Mujib Medical University Journal ◽

10.3329/bsmmuj.v12i2.41229 ◽

2019 ◽

Vol 12 (2) ◽

pp. 105-108

Author(s):

Sabera Khatun ◽

Sayada Fatema Khatun

Keyword(s):

Cervical Cancer ◽

Cancer Screening ◽

Cancer Patients ◽

Cervical Cancer Screening ◽

False Negative ◽

Precancerous Lesion ◽

True Negative ◽

Liquid Based Cytology

The aim of this study was to screen the suspected cervical cancer patients (n=100) by liquid-based cytology and conventional pap’s smear followed by colposcopic biopsy from July 2016 to June 2017. In conventional pap’s test, 73 cases were true negative whereas 25 cases were false negative. However, in liquid-based cytology, 68 cases were true negative and 23 cases were false negative. Finally when colposcopic examinations were done, 61 cases were true negative and 15 cases were false negative. The sensitivity of liquid-based cytology was 11.5% for cervical cancer screening which was more than the conventional pap’s smear (3.8%). In conclusion, liquid-based cytology should be more preferable method than the than conventional pap’s smear for the diagnosis of precancerous lesion of the cervix.

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Training and validation study for sequential monitoring of CAMLs in circulation to predict ongoing progression in lung cancer patients undergoing definitive radiotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.3053 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 3053-3053

Author(s):

Daniel Adams ◽

Jianzhong He ◽

Yawei Qiao ◽

Ting Xu ◽

Hui Gao ◽

...

Keyword(s):

Lung Cancer ◽

Radiation Therapy ◽

Cancer Patients ◽

Stage Iv ◽

Progression Free Survival ◽

Training Set ◽

Lung Cancer Patients ◽

Therapeutic Decisions ◽

Validation Set ◽

Nsclc Patients

3053 Background: Cancer Associated Macrophage-Like cells (CAMLs) are a recently described circulating stromal cell common in the peripheral blood of cancer patients that are prognostic for progressive disease. Further, it has been shown that changes in CAML size (i.e. enlargement above 50µm) can predict progression free survival (PFS) in thoracic cancers (e.g. lung). We enrolled 104 unresectable non-small cell lung cancer (NSCLC) patients, with an initial training set review of 54 patients, to determine if change in CAML size after radiation therapy was predictive PFS. Methods: A 2 year single blind prospective study was undertaken to test the relationship of ≥50µm CAMLs to PFS based on imaging in lung patients before and after induction of chemo radiation, or radiation therapy. To achieve a 2-tailed 90% power (α = 0.05) we recruited a training set of 54 patients and validation set of 50 patients all with pathologically confirmed unresectable NSCLC: Stage I (n = 14), Stage II (n = 16), Stage III (n = 61) & Stage IV (n = 13). Baseline (BL) blood samples were taken prior to start of therapy & a 2nd blood sample (T1) was taken after completion of radiotherapy (~30 days). Blood was filtered by CellSieve filtration and CAMLs quantified. Analysis by CAML size of < 49 µm or ≥50 µm was used to evaluate PFS hazard ratios (HRs) by censored univariate & multivariate analysis. Results: CAMLs were found in 95% of samples averaging 2.7 CAMLs/7.5mL sample at BL, with CAMLs ≥50 µm having reduced PFS (HR = 2.2, 95%CI1.3-3.8, p = 0.003). At T1, 18 patients had increased CAML size ≥50 µm with PFS (HR = 4.6, 95%CI2.5-8.3, p < 0.001). In total, ≥50 µm CAMLs at BL was 76% accurate at predicting progression within 24 months while ≥50 µm CAMLs at T1 was 83% accurate at predicting progression. Conclusions: In unresectable NSCLC patients, enlargement of CAMLs during treatment is an indicator active progression. We identify that a single ≥50 µm CAML after induction of radiotherapy, in our training set and confirmed in our validation set, is an indicator of poor prognosis. We suggest that changes in CAML size during therapy may indicate the efficacy of therapy and could potentially help shape subsequent therapeutic decisions.

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Endoscopic third ventriculostomy in the treatment of childhood hydrocephalus in Uganda: report of a scoring system that predicts success

Journal of Neurosurgery Pediatrics ◽

10.3171/2009.9.peds09196 ◽

2010 ◽

Vol 5 (2) ◽

pp. 143-148 ◽

Cited By ~ 94

Author(s):

Benjamin C. Warf ◽

John Mugamba ◽

Abhaya V. Kulkarni

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Endoscopic Third Ventriculostomy ◽

Successful Outcome ◽

Third Ventriculostomy ◽

P Value ◽

Training Set ◽

Childhood Hydrocephalus ◽

Validation Set

Object In Uganda, childhood hydrocephalus is common and difficult to treat. In some children, endoscopic third ventriculostomy (ETV) can be successful and avoid dependence on a shunt. This can be especially beneficial in Uganda, because of the high risk of infection and long-term failure associated with shunting. Therefore, the authors developed and validated a model to predict the chances of ETV success, taking into account the unique characteristics of a large sub-Saharan African population. Methods All children presenting with hydrocephalus at CURE Children's Hospital of Uganda (CCHU) between 2001 and 2007 were offered ETV as first-line treatment and were prospectively followed up. A multivariable logistic regression model was built using ETV success at 6 months as the outcome. The model was derived on 70% of the sample (training set) and validated on the remaining 30% (validation set). Results Endoscopic third ventriculostomy was attempted in 1406 patients. Of these, 427 were lost to follow-up prior to 6 months. In the remaining 979 patients, the ETV was aborted in 281 due to poor anatomy/visibility and in 310 the ETV failed during the first 6 months. Therefore, a total of 388 of 979 (39.6% and [55.6% of completed ETVs]) procedures were successful at 6 months. The mean age at ETV was 12.6 months, and 57.8% of cases were postinfectious in origin. The authors' logistic regression model contained the following significant variables: patient age at ETV, cause of hydrocephalus, and whether choroid plexus cauterization was performed. In the training set (676 patients) and validation set (303 patients), the model was able to accurately predict the probability of successful ETV (Hosmer-Lemeshow p value > 0.60 and C statistic > 0.70). The authors developed the simplified CCHU ETV Success Score that can be used in the field to predict the probability of ETV success. Conclusions The authors' model will allow clinicians to accurately identify children with a good chance of successful outcome with ETV, taking into account the unique characteristics and circumstances of the Ugandan population.

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A Novel Systematic Oxidative Stress Score Predicts the Prognosis of Patients with Operable Breast Cancer

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/9441896 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Kaiming Zhang ◽

Liqin Ping ◽

Tian Du ◽

Yan Wang ◽

Ya Sun ◽

...

Keyword(s):

Breast Cancer ◽

Oxidative Stress ◽

Cancer Patients ◽

Operable Breast Cancer ◽

Breast Cancer Patients ◽

Training Set ◽

Oxidative Stress Biomarkers ◽

Stress Biomarkers ◽

Validation Set ◽

The Relationship

Background. Breast cancer was associated with imbalance between oxidation and antioxidation. Local oxidative stress in tumors is closely related to the occurrence and development of breast cancer. However, the relationship between systematic oxidative stress and breast cancer remains unclear. This study is aimed at exploring the prognostic value of systematic oxidative stress in patients with operable breast cancer. Methods. A total of 1583 operable female breast cancer patients were randomly assigned into the training set and validation set. The relationship between systematic oxidative stress biomarkers and prognosis were analyzed in the training and validation sets. Results. The systematic oxidative stress score (SOS) was established based on five systematic oxidative stress biomarkers including serum creatinine (CRE), serum albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN). SOS was an independent prognostic factor for operable breast cancer patients. A nomogram based on SOS and clinical characteristics could accurately predict the prognosis of operable breast cancer patients, and the area under the curve (AUC) of the nomogram was 0.823 in the training set and 0.872 in the validation set, which was much higher than the traditional prognostic indicators. Conclusions. SOS is an independent prognostic indicator for operable breast cancer patients. A prediction model based on SOS could accurately predict the outcome of operable breast cancer patients.

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