Clinical Outcomes and Prognostic Factors in Stage III C Cervical Cancer Patients Treated with Radical Radiotherapy or Radiochemotherapy.

Objective: Since the update of the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging criteria, there have been few reports on the prognosis of stage III C cervical cancer. Moreover, some studies have drawn controversial conclusions, necessitating further verification. This study aims to evaluate the clinical outcomes and determine the prognostic factors for stage III C cervical cancer patients treated with radical radiotherapy or radiochemotherapy. Methods: The data of 117 stage III C cervical cancer patients (98 III C1 and 19 III C2) who underwent radical radiotherapy or radiochemotherapy were retrospectively analyzed. We evaluated 3-year overall survival (OS) and disease-free survival (DFS) using the Kaplan-Meier method. Prognostic factors were analyzed using the Log-rank test and Cox proportional hazard regression model. The risk of para-aortic lymph node metastasis (LNM) in all patients was assessed through Chi-squared test and logistic regression analysis. Results: For stage III C1 and III C2 patients, the 3-year OS rates were 77.6% and 63.2% (P = .042), and the 3-year DFS rates were 70.4% and 47.4% (P = .003), respectively. The pretreatment location of pelvic LNM, histological type, and FIGO stage was associated with OS (P = .033, .003, .042, respectively); the number of pelvic LNM and FIGO stage were associated with DFS (P = .015, .003, respectively). The histological type was an independent prognostic indicator for OS, and the numbers of pelvic LNM and FIGO stage were independent prognostic indicators for DFS. Furthermore, a pelvic LNM largest short-axis diameter ≥ 1.5 cm and the presence of common iliac LNM were identified as high-risk factors influencing para-aortic LNM in stage III C patients (P = .046, .006, respectively). Conclusions: The results of this study validated the 2018 FIGO staging criteria for stage III C cervical cancer patients undergoing concurrent chemoradiotherapy. These findings may enhance our understanding of the updated staging criteria and contribute to better management of patients in stage III C.

Clinicopathological Characteristics of Breast Cancer Patients with HER-2 Low Expression Receiving Neoadjuvant Therapy.

INTRODUCTION: Human epidermal growth factor receptor-2 (HER-2) low expression breast malignant tumors have become a research hotspot in recent years, but it is still unclear whether HER-2 low expression represents a special subtype of breast cancer. However, this molecular type requires more effective treatment regimens in the neoadjuvant therapy stage. METHODS: This study enrolled breast cancer patients who were treated at Harbin Medical University Cancer Hospital with neoadjuvant treatment between October 2011 and May 2019 and was a single-center retrospective study. RESULTS: A total of 1,053 breast cancer patients who received preoperative therapy, including 279 (26%) HER-2 low expression patients, were included in this retrospective study. The HER-2 low expression group had a higher proportion of patients under 50 years old than the other two molecular subtype groups (p = 0.047, 62.0% vs. 57.2% and 52.5%), and the percentage of patients with Ki67 index above 15% was lower than that in HER-2-negative and HER-2-positive patients (p < 0.001, 50.2% vs. 63.6% and 71.5%). Most of the patients with HER-2 low expression were hormone receptor (HR) positive (p < 0.001, 85.7% vs. 60.4% and 36.0%), and their pathologic complete response (pCR) rate after neoadjuvant therapy was significantly lower than that of HER-2-negative and HER-2-positive patients (p < 0.001, 5.7% vs. 11.8% and 20.5%). The results of the subgroup analysis showed HR-positive patients with HER-2 low expression had a lower pCR rate (p < 0.001, 4.6% vs. 14.6%) and objective response rate (p = 0.001, 77.8% vs. 91.0%) than HER-2-positive patients and had no significant difference in these rates compared to HER-2-negative patients. There were no significant differences in overall survival (OS) and disease-free survival (DFS) up to 67 months (the median follow-up time) among HER-2 low, HER-2-negative, and HER-2-positive patients. The results of Cox hazard proportional showed that the Ki67 index and T stage (T3) were independent influencing factors for DFS. In terms of OS, Ki67 index, P53, T stage, and objective response were independent influencing factors for OS in HER-2 low expression patients. CONCLUSIONS: In general, further studies are needed to confirm that HER-2 low expression is a special breast cancer molecular subtype. The efficacy of neoadjuvant therapy in patients with HER-2 low expression is relatively poor, and the efficacy of neoadjuvant therapy can predict the prognosis of patients with HER-2 low expression.

Naples score: a novel prognostic biomarker for breast cancer patients undergoing neoadjuvant chemotherapy.

BACKGROUND AND PURPOSE: The Naples Score (NPS) is a novel prognostic indicator that has been used in various cancers, but its potential in breast malignant tumor patients receiving neoadjuvant chemotherapy (NAC) has not been discovered. This study aimed to investigate the relationship between NPS and overall survival (OS) and disease-free survival (DFS) in breast cancer patients. METHODS: A total of 217 breast cancer patients undergoing NAC were incorporated into this retrospectively research. K-M survival curves and log-rank tests are used to determine OS and DFS. Cox regression model was used to evaluate the relationship between NPS and OS and DFS. Nomogram was developed based on the results of multivariate Cox regression analysis. Prognostic models were internally validated using bootstrapping and the consistency index (C-index). RESULTS: Age group was correlated with NPS (p < 0.05). Low and moderate Naples risk patients had higher 5-year OS and DFS rates than high risk Naples patients (93.8% vs. 75.4% vs. 60.0%; X2 = 9.2, P = 0.01; 82.4% vs 64.5% vs 43.7%; X2 = 7.4, P = 0.024; respectively). The nomogram based on demonstrated good performance in predicting OS and DFS (AUC = 0.728, 0.630; respectively). CONCLUSIONS: In breast cancer patients who have undergone NAC, NPS is a novel prognostic indicator. NPS combined with clinicopathological features showed good predictive ability, and its performance was better than that of traditional pathological TNM staging.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning.

BACKGROUND: Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients. METHODS: From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared. RESULTS: NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706). CONCLUSIONS: The ML model XGBoost can well predict NSLNM in breast cancer patients.

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy.

Background: It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning. Methods: This retrospective analysis included 249 patients with malignant breast tumors undergoing neoadjuvant chemotherapy (NAC). After comparing seven potent machine learning models, the best model, Xgboost, was applied to construct an INPS system. K-M survival curves and the log-rank test were employed to determine OS and DFS. Univariate and multivariate analyses were carried out with the Cox regression model. Additionally, we compared the predictive power of INPS, inflammatory, and standard nutritional variables using the Z test. Results: After comparing seven machine learning models, it was determined that the XGBoost model had the best OS and DFS performance (AUC = 0.865 and 0.771, respectively). For overall survival (OS, cutoff value = 0.3917) and disease-free survival (cutoff value = 0.4896), all patients were divided into two groups by the INPS. Those with low INPS had higher 5-year OS and DFS rates (77.2% vs. 50.0%, P < 0.0001; and 59.6% vs. 32.1%, P < 0.0001, respectively) than patients with high INPS. For OS and DFS, the INPS exhibited the highest AUC compared to the other inflammatory and nutritional variables (AUC = 0.615, P = 0.0003; AUC = 0.596, P = 0.0003, respectively). Conclusion: The INPS was an independent predictor of OS and DFS and exhibited better predictive ability than BMI, PNI, and MLR. For patients undergoing NAC for nonpCR breast cancer, INPS was a crucial and comprehensive biomarker. It could also forecast individual survival in breast cancer patients with low HER-2 expression.

Prognostic value of a modified systemic inflammation score in breast cancer patients who underwent neoadjuvant chemotherapy.

BACKGROUND AND PURPOSE: The modified systemic inflammation score (mSIS) system, which is constructed based on the neutrophil to lymphocyte ratio (NLR) and albumin (Alb), has not been applied to evaluate the prognosis of malignant breast cancer patients who underwent neoadjuvant chemotherapy (NAC). The present study aimed to explore the relationship between the mSIS and overall survival (OS), disease-free survival (DFS) and pathological complete response (pCR). METHODS: A total of 305 malignant breast tumor patients who underwent NAC were incorporated into this retrospective analysis. We determined OS and DFS using K-M survival curves and the log-rank test. The relationship between the mSIS and OS and DFS was evaluated by a Cox regression model. A nomogram was constructed based on Cox regression analysis. RESULTS: Patients in the mSIS low-risk group had better 5- and 8-year OS rates than those in the mSIS high-risk group (59.8% vs. 77.0%; 50.1% vs. 67.7%; X2 = 8.5, P = 0.0035, respectively). Patients in the mSIS (1 + 2 score) + pCR subgroup had the highest 5- and 8-year OS and disease-free survival (DFS) rates (OS: 55.0% vs. 75.7% vs. 84.8, 42.8% vs. 65.7% vs. 79.8%, X2 = 16.6, P = 0.00025; DFS: 38.8% vs. 54.7% vs. 76.3%, 33.3% vs. 42.3 vs. 72.1%, X2 = 12.4, P = 0.002, respectively). Based on the mSIS, clinical T stage and pCR results, the nomogram had better predictive ability than the clinical TNM stage, NLR and Alb. CONCLUSIONS: mSIS is a promising prognostic tool for malignant breast tumor patients who underwent NAC, and the combination of mSIS and pCR is helpful in enhancing the ability to predict a pCR.

Prognostic factors for IB2-IIIB cervical cancer patients treated by radiation therapy with high-dose-rate brachytherapy in a single-institution study.

Purpose: To evaluate the efficacy of radiotherapy in locally advanced cervical cancer, and to determine the factors affecting prognosis. Material and methods: Clinical data of 211 patients with cervical cancer, treated at our institution between June 2014 and February 2017 were reviewed retrospectively. All patients were treated with definitive radiotherapy and received external irradiation of 45-50.4 Gy. High-dose-rate brachytherapy (HDR-BT) of 24-36 Gy was prescribed to a high-risk clinical target volume (HR-CTV) as a local boost. All statistical analyses were performed with SPSS version 19.0 using Kaplan-Meier survival test and Cox regression analysis. Additionally, dose parameters of patients with IIIB stage treated with combined intracavitary/interstitial (IC/IS) implants were compared with IC only. Results: With a median follow-up time of 69 months, local control (LC), overall survival (OS), disease-free survival (DFS), and nodal control (NC) at 5 years were 89%, 78%, 67%, and 88%, respectively. In multivariate analysis, the major determinant of LC was the level of pre-treatment squamous cell carcinoma antigen (SCC-Ag). The predictors of shorter OS were adenocarcinoma, pre-treatment SCC-Ag, and FIGO stage. Worse DFS was associated with adenocarcinoma, pre-treatment SCC-Ag, and involved lymph nodes. The predictors for nodal failure were positive pelvic lymph nodes. Patients with IIIB treated with IC/IS brachytherapy tended to improve DFS compared with IC alone, and obtained similar HR-CTV D90 EQD2 (n = 10) and biological effective dose (BED), 91 ±6 Gy vs. 89 ±3 Gy, and 107 ±4.5 Gy vs. 107 ±5.6 Gy, whereas decreased organs at risk (OARs) doses, including rectum and bladder D2cm3 were 7.5 Gy and 7.2 Gy lower, respectively. Late grade 3-4 bladder and bowel toxicities were observed in 1.9% of patients. Conclusions: Radiation therapy carried out in our institution results in good survival, with acceptable toxicity in locally advanced cervical cancer. Different individualized therapeutic strategies should be considered for patients with high-risk factors.

Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework.

Abstract: Background and purpose: Machine learning (ML) is applied for outcome prediction and treatment support. This study aims to develop different ML models to predict risk of axillary lymph node metastasis (LNM) in breast invasive micropapillary carcinoma (IMPC) and to explore the risk factors of LNM. Methods: From the Surveillance, Epidemiology, and End Results (SEER) database and the records of our hospital, a total of 1547 patients diagnosed with breast IMPC were incorporated in this study. The ML model is built and the external validation is carried out. SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model; multivariable analysis was performed with logistic regression (LR); and nomograms were constructed according to the results of LR analysis. Results: Age and tumor size were correlated with LNM in both cohorts. The luminal subtype is the most common in patients, with the tumor size <=20mm. Compared to other models, Xgboost was the best ML model with the biggest AUC of 0.813 (95% CI: 0.7994 - 0.8262) and the smallest Brier score of 0.186 (95% CI: 0.799-0.826). SHAP plots demonstrated that tumor size was the most vital risk factor for LNM. In both training and test sets, Xgboost had better AUC (0.761 vs 0.745; 0.813 vs 0.775; respectively), and it also achieved a smaller Brier score (0.202 vs 0.204; 0.186 vs 0.191; 0.220 vs 0.221; respectively) than the nomogram model based on LR in those three different sets. After adjusting for five most influential variables (tumor size, age, ER, HER-2, and PR), prediction score based on the Xgboost model was still correlated with LNM (adjusted OR:2.73, 95% CI: 1.30-5.71, P=0.008). Conclusions: The Xgboost model outperforms the traditional LR-based nomogram model in predicting the LNM of IMPC patients. Combined with SHAP, it can more intuitively reflect the influence of different variables on the LNM. The tumor size was the most important risk factor of LNM for breast IMPC patients. The prediction score obtained by the Xgboost model could be a good indicator for LNM.

The Pretreatment Systemic Inflammation Response Index as a Useful Prognostic Factor is Better Than Lymphocyte to Monocyte Ratio in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy.

It has been reported that the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR), as well as systemic inflammation response index (SIRI), are closely related with overall survival (OS) in breast cancer patients. However, which one is the optimal indicator is vague. This study incorporates 280 breast cancer patients who received NACT. A cut-off value of LMR, PLR, SIRI and NLR is determined by Youden index. The Pearson's X2 test or Fisher's exact test is applied to compare the correlation of different clinicopathologic characteristics divided by SIRI. The K-M survival curves and log-rank test were applied to determine OS. Univariate and multivariable analysis are explored by the Cox regression model. We apply the Z test to contrast the prognostic capacity of SIRI, LMR, PLR, and NLR. At the meanwhile, we construct the nomogram based on the results of multivariable analysis. All enrolled cases are divided into two parts by pretreatment SIRI (cut-off value = 0.52). Compared to high pre-treatment SIRI, high pre-treatment NLR and clinical T3 + T4 stage, the low pre-treatment SIRI, low pretreatment NLR and clinical T1 + T2 stage had longer OS time. The Z test showed that the SIRI group had bigger AUC than LMR and PLR, and the difference is statistically significant. The ability of nomogram, based on pretreatment SIRI, pre-treatment NLR and clinical T stage, to predict the 3-year, 5-year, and 8-year overall survival rates of breast malignant tumor patients is better than clinical TNM stage. Pre-treatment SIRI was a more crucial and integral prognostic index for breast malignant tumor patients receiving NACT. It could be helpful for doctors to predict the prognosis of breast malignant tumor patients and to evaluate the treatment status of patients.

Diagnostic Value of the Survivin Autoantibody in Four Types of Malignancies.

BACKGROUND: Tumor-associated antigen overexpression, which has been reported in many types of cancers, may trigger autoantibody secretion. The present study was designed to test whether levels of circulating autoantibodies to survivin protein-derived antigens is altered in liver, esophageal, breast, and lung cancers. METHODS: Patients with liver (144), esophageal (159), breast (124), and lung cancers (267), and healthy volunteers (362) were recruited for the study, and serum samples were collected for ELISA autoantibody analysis. RESULTS: Compared with the control group, survivin autoantibody levels were significantly higher in serum from patients with breast cancer and lung cancer, but were significantly lower in serum from patients with liver cancer (p < 0.05). In stage I and II lung cancer, the best-fit areas under the receiver operating characteristic curve was 0.731 (standard error [SE] = 0.023; 95% confidence interval [CI] 0.687-0.776) and the sensitivity, with 90% specificity, was 23.7%. CONCLUSION: Analysis across four types of malignancies revealed that the survivin autoantibody had good specificity and sensitivity in lung cancer. Circulating autoantibodies to survivin could be a potential biomarker for the early lung cancer diagnosis.