Four conserved amino acids on human papillomavirus E6 predict clinical high-risk types.

Human papillomavirus (HPV) types included in the genus alpha papillomavirus (alpha-HPVs) are subdivided into high- and low-risk HPVs associated with tumorigenicity. According to conventional risk classification, over 30 alpha-HPVs remain unclassified and HPV groups phylogenetically classified using the L1 gene do not exactly correspond to the conventional risk classification groups. Here, we propose a novel cervical lesion progression risk classification strategy. Using four E6 risk distinguishable amino acids (E6-RDAAs), we successfully expanded the conventional classification to encompass alpha-HPVs and resolve discrepancies. We validated our classification system using alpha-HPV-targeted sequence data of 325 cervical swab specimens from participants in Japan. Clinical outcomes significantly correlated with the E6-RDAA classification. Four of five HPV types in the data set that were not conventionally classified (HPV30, 34, 67, and 69) were high-risk according to our classification criteria. This report sheds light on the carcinogenicity of rare genital HPV types using a novel risk classification strategy.

A novel nomogram and risk classification system for predicting overall survival in head and neck squamous cell cancer with distant metastasis at initial diagnosis.

INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC) is one of the most invasive cancer types globally, and distant metastasis (DM) is associated with a poor prognosis. The objective of this study was designed to construct a novel nomogram and risk classification system to predict overall survival (OS) in HNSCC patients presenting with DM at initial diagnosis. METHODS: HNSCC patients with initially diagnosed DM between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Firstly, all patients were randomly assigned to a training cohort and validation cohort (8:2), respectively. The Cox proportional hazards regression model was used to analyze the prognostic factors associated with OS. Then, the nomogram based on the prognostic factors and the predictive ability of the nomogram were assessed by the calibration curves, receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Finally, a risk classification system was established according to the nomogram scores. RESULTS: A total of 1240 patients initially diagnosed with HNSCC with DM were included, and the 6-, 12- and 18-month OS of HNSCC with DM were 62.7%, 40.8% and 30%, respectively. The independent prognostic factors for HNSCC patients with DM included age, marital status, primary site, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, radiotherapy and chemotherapy. Based on the independent prognostic factors, a nomogram was constructed to predict OS in HNSCC patients with DM. The C-index values of the nomogram were 0.713 in the training cohort and 0.674 in the validation cohort, respectively. The calibration curves and DCA also indicated the good predictability of the nomogram. Finally, a risk classification system was built and it revealed a statistically significant difference among the three groups of patients according to the nomogram scores. CONCLUSIONS: Factors associated with the overall survival of HNSCC patients with DM were found. According to the identified factors, we generated a nomogram and risk classification system to predict the OS of patients with initially diagnosed HNSCC with DM. The prognostic nomogram and risk classification system can help to assess survival time and provide guidance when making treatment decisions for HNSCC patients with DM.

Risk analysis of grade ≥ 2 radiation pneumonitis based on radiotherapy timeline in stage III/IV non-small cell lung cancer treated with volumetric modulated arc therapy: a retrospective study.

BACKGROUND: Radiotherapy is an important treatment for patients with stage III/IV non-small cell lung cancer (NSCLC), and due to its high incidence of radiation pneumonitis, it is essential to identify high-risk people as early as possible. The present work investigates the value of the application of different phase data throughout the radiotherapy process in analyzing risk of grade ≥ 2 radiation pneumonitis in stage III/IV NSCLC. Furthermore, the phase data fusion was gradually performed with the radiotherapy timeline to develop a risk assessment model. METHODS: This study retrospectively collected data from 91 stage III/IV NSCLC cases treated with Volumetric modulated arc therapy (VMAT). Patient data were collected according to the radiotherapy timeline for four phases: clinical characteristics, radiomics features, radiation dosimetry parameters, and hematological indexes during treatment. Risk assessment models for single-phase and stepwise fusion phases were established according to logistic regression. In addition, a nomogram of the final fusion phase model and risk classification system was generated. Receiver operating characteristic (ROC), decision curve, and calibration curve analysis were conducted to internally validate the nomogram to analyze its discrimination. RESULTS: Smoking status, PTV and lung radiomics feature, lung and esophageal dosimetry parameters, and platelets at the third week of radiotherapy were independent risk factors for the four single-phase models. The ROC result analysis of the risk assessment models created by stepwise phase fusion were: (area under curve [AUC]: 0.67,95% confidence interval [CI]: 0.52-0.81), (AUC: 0.82,95%CI: 0.70-0.94), (AUC: 0.90,95%CI: 0.80-1.00), and (AUC:0.90,95%CI: 0.80-1.00), respectively. The nomogram based on the final fusion phase model was validated using calibration curve analysis and decision curve analysis, demonstrating good consistency and clinical utility. The nomogram-based risk classification system could correctly classify cases into three diverse risk groups: low-(ratio:3.6%; 0 < score < 135), intermediate-(ratio:30.7%, 135 < score < 160) and high-risk group (ratio:80.0%, score > 160). CONCLUSIONS: In our study, the risk assessment model makes it easy for physicians to assess the risk of grade ≥ 2 radiation pneumonitis at various phases in the radiotherapy process, and the risk classification system and nomogram identify the patient's risk level after completion of radiation therapy.

Impact of radiation dose on recurrence in high-risk prostate cancer patients.

BACKGROUND: Dose escalated radiation therapy (RT) combined with long-term androgen deprivation therapy (ADT) is a standard of care option for men with high-risk and locally advanced prostate cancer (PCa). However, the optimal dose of escalated RT and ADT is not known. Here we assessed the impact of radiation dose and length of ADT on biochemical recurrence in a multi-institutional cohort stratified by the Cambridge prognostic group (CPG). We hypothesized that radiation dose and length of ADT would impact outcome in similar risk groups of our patients. METHODS: Two-hundred and forty-four patients were included, 132 from Oslo University Hospital, Department of Oncology and 112 from Johns Hopkins Hospital, Department of Radiation Oncology. Biochemical recurrence was defined as prostate-specific antigen (PSA) nadir +2 ng/mL. Time to recurrence was estimated using Kaplan-Meier analysis and when stratified by CPG the log-rank test was used. Cox regression analysis was performed to identify factors associated with risk of recurrence. Site of recurrence was investigated. RESULTS: The median follow-up time was 7.4 years. The vast majority (71%) of patients were classified as high-risk (CPG 4) or very high-risk features (CPG 5). Significantly more PSA recurrences occurred in CPG 5 (41%) compared with CPG 4 (25%) (P = .04) and five-year cumulative recurrence-free survival was lower for CPG 4 and 5 (89% and 68%) compared with CPG 1, 2, and 3 (100%, 100%, and 93%). The recurrence-free survival for CPG 5 was significantly higher for prostate irradiation of 80 Gy as compared with 74 Gy (P = .011). For CPG 4 and 5 no local recurrences were detected in patients receiving 80 Gy. On stepwise Cox regression analysis neither age nor length of ADT were independent prognostic factors for recurrence free survival. CONCLUSION: Prostate dose escalation from 74 to 80 Gy decreases risk of recurrence in high-risk PCa. Further studies are needed to identify the optimal combination of ADT and RT.

Novel nomograms for castration-resistant prostate cancer and survival outcome in patients with de novo bone metastatic prostate cancer.

OBJECTIVES: To develop nomograms predicting the incidence of castration-resistant prostate cancer (CRPC) and overall survival (OS) for de novo metastatic prostate cancer (PCa). PATIENTS AND METHODS: Data from 449 patients with de novo metastatic PCa were retrospectively analysed. Patients were randomly divided into a training (n = 314, 70%) and a validation cohort (n = 135, 30%). Predictive factors were selected using a Cox proportional hazards model and were further used for building predictive models. The outcomes were incidence of CRPC and OS. RESULTS: Predictive factors included: Gleason score (GS), intraductal carcinoma of the prostate (IDC-P), Eastern Cooperative Oncology Group status, and alkaline phosphatase, haemoglobin and prostate-specific antigen levels. IDC-P and GS were the strongest prognosticators for both the incidence of CRPC and OS. Nomograms for predicting CRPC and OS had an internal validated concordance index of 0.762 and 0.723, respectively. Based on the ß coefficients of the final model, risk classification systems were constructed. For those with favourable, intermediate and poor prognosis, the median time to CRPC was 62.6, 28.0 and 13.0 months (P < 0.001), respectively; and the median OS was not reached, 55.0 and 33.0 months, respectively (P < 0.001). CONCLUSIONS: We developed two novel nomograms to predict the incidence of CRPC and OS for patients with de novo metastatic PCa. These tools may assist in physician decision-making and the designing of clinical trials.

The assessment of different risk classification systems for gastrointestinal stromal tumors (GISTs): the analytic results from the SEER database.

BACKGROUND: Although various risk classification systems for GISTs have been proposed, the optimum one remains uncertain. In the present study, we compared the prognostic stratification of different risk classification systems for GIST patients. METHODS: We reviewed those patients who were pathologically diagnosed with GISTs in the SEER database between 2009 and 2014. All patients were classified into different risk groups according to the NIH criteria, AFIP criteria and AJCC staging system, respectively. The prognostic differences between different risk groups were compared and clinicopathologic features were analyzed. RESULTS: The prognosis of small intestinal GISTs was not significantly different from that of gastric GISTs. For gastric GIST patients, there was no significant prognostic difference between very low risk and low risk group according to the NIH and AFIP criteria. However, the prognostic stratification for two groups could be improved by the AJCC staging system. For small intestinal GIST patients, the prognostic difference between low risk and intermediate risk group was not stratified properly by the NIH and AFIP criteria. However, the prognostic difference between two groups could reach statistical significance according to the AJCC staging system. Unlike gastric GISTs, tumor size was not identified as an independent factor influencing the prognosis of small intestinal GISTs. CONCLUSIONS: The AJCC staging system could provide a better prognostic stratification for GIST patients compared with the NIH and AFIP criteria, regardless of gastric or small intestinal tumor. However, primary tumor location and tumor size may be reconsidered and revised in the risk classification system.

A novel postoperative nomogram and risk classification system for individualized estimation of survival among patients with parotid gland carcinoma after surgery.

BACKGROUND: Parotid gland carcinoma (PGC) is a rare but aggressive head and neck cancer, and the prognostic model associated with survival after surgical resection has not yet been established. This study aimed to construct a novel postoperative nomogram and risk classification system for the individualized prediction of overall survival (OS) among patients with resected PGC. METHODS: Patients with PGC who underwent surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were randomized into training and validation cohorts (7:3). A nomogram developed using independent prognostic factors based on the results of the multivariate Cox regression analysis. Harrell's concordance index (C-index), time-dependent area under the curve (AUC), and calibration plots were used to validate the performance of the nomogram. Moreover, decision curve analysis (DCA) was performed to compare the clinical use of the nomogram with that of traditional TNM staging. RESULTS: In this study, 5077 patients who underwent surgery for PGC were included. Age, sex, marital status, tumor grade, histology, TNM stage, surgery type, radiotherapy, and chemotherapy were independent prognostic factors. Based on these independent factors, a postoperative nomogram was developed. The C-index of the proposed nomogram was 0.807 (95% confidence interval 0.797-0.817). Meanwhile, the time-dependent AUC (> 0.8) indicated that the nomogram had a satisfactory discriminative ability. The calibration curves showed good concordance between the predicted and actual probabilities of OS, and DCA curves indicated that the nomogram had a better clinical application value than the traditional TNM staging. Moreover, a risk classification system was built that could perfectly classify patients with PGC into three risk groups. CONCLUSIONS: This study constructed a novel postoperative nomogram and corresponding risk classification system to predict the OS of patients with PGC after surgery. These tools can be used to stratify patients with high or low risk of mortality and provide high-risk patients with more directed therapies and closer follow-up.

Construction of novel predictive tools for post-surgical cancer-specific survival probability in patients with primary chondrosarcoma and external validation in Chinese cohorts: a large population-based retrospective study.

BACKGROUND: Surgery is the predominant treatment modality for chondrosarcoma. This study aims to construct a novel clinic predictive tool that accurately predicts the 3-, 5-, and 8-year probability of cancer-specific survival (CSS) for primary chondrosarcoma patients who have undergone surgical treatment. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 982 primary chondrosarcoma patients after surgery, who were randomly divided into two sets: training set (60%) and internal validation set (40%). Cox proportional regression analyses were used to screen post-surgical independent prognostic variables in primary chondrosarcoma patients. These identified variables were used to construct a nomogram to predict the probability of post-surgical CSS of primary chondrosarcoma patients. The k-fold cross-validation method (k = 10), Harrell's concordance index (C-index), receiver operating characteristic curve (ROC) and area under curve (AUC) were used to assess the predictive accuracy of the nomogram. Calibration curve and decision curve analysis (DCA) were used to validate the clinical application of the nomogram. RESULTS: Age, tumor size, disease stage and histological type were finally identified post-surgical independent prognostic variables. Based the above variables, a nomogram was constructed to predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. The results of the C-index showed excellent predictive performance of the nomogram (training set: 0.837, 95% CI: 0.766-0.908; internal validation set: 0.835, 95% CI: 0.733-0.937; external validation set: 0.869, 95% CI: 0.740-0.998). The AUCs of ROC were all greater than 0.830 which again indicated that the nomogram had excellent predictive performance. The results of calibration curve and DCA indicated that the clinical applicability of this nomogram was outstanding. Finally, the risk classification system and online access version of the nomogram was developed. CONCLUSION: We constructed the first nomogram to accurately predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. This nomogram would assist surgeons to provide individualized post-surgical survival predictions and clinical strategies for primary chondrosarcoma patients.

Development and validation of a prognostic nomogram for breast cancer patients who underwent chemoradiotherapy and surgery: a retrospective cohort study based on the SEER database and two Chinese cohorts.

There is no strong evidence indicating the optimal treatment for breast cancer (BC) and no specific prognostic model. The aim of this study was to establish nomograms to predict the overall survival (OS) of BC patients receiving chemoradiotherapy and surgery, thereby quantifying survival benefits and improving patient management. A total of 1877 patients with primary nonmetastatic BC who received chemoradiotherapy and surgery from 2010 to 2019 were identified from the Surveillance, Epidemiology and End Results (SEER) database as the training cohort, 804 as the internal validation cohort, and 796 patients from the First Affiliated Hospital of Zhengzhou University (n=324) and Jiaxing Maternal and Child Health Hospital (n=472) as the external validation cohort. Least absolute shrinkage and selection operator (LASSO), univariate, and multivariate Cox regression analyses were performed in the training cohort to determine independent prognostic factors for BC, and a nomogram was constructed to predict 3-year, 5-year, and 8-year OS. The final model incorporated 7 factors that significantly affect OS: race, location, positive regional nodes, T stage, N stage, subtype, and grade. The calibration curves showed good consistency between the predicted survival and actual outcomes. Time-dependent receiver operating characteristic (ROC) curves and the time-dependent area under the curve (AUC) confirmed that the accuracy and clinical usefulness of the constructed nomograms were favorable. Decision curve analysis (DCA) and net reclassification improvement (NRI) also demonstrated that this nomogram was more suitable for clinical use than the 7th American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system and the previous prediction model. In the training cohort and the internal validation cohort, the concordance indices (C-index) of the nomogram for predicting OS (0.723 and 0.649, respectively) were greater than those of the 7th AJCC TNM staging system and the previous prediction model. In addition, based on Kaplan-Meier (K-M) survival curves, the survival differences among different risk stratifications were statistically significant, indicating that our risk model was accurate. In this study, we determined independent prognostic factors for OS in patients with primary nonmetastatic BC treated with chemoradiotherapy and surgery. A new and accurate nomogram for predicting 3-, 5-, and 8-year OS in this patient population was developed and validated for potential clinical applicability.

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone.

BACKGROUND: Osteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients. METHOD: We studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram. RESULTS: Age, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups. CONCLUSION: We established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.

Endometrial clear cell carcinoma: A population-based study.

Background: A systematic analysis of prognostic factors concerning endometrial clear cell carcinoma (ECCC) is lacking. The current study aimed to construct nomograms predicting the overall survival (OS) of ECCC patients. Methods: We performed a retrospective study, and predicted nomograms for 3-, 5-, and 10-year OS were established. The nomograms were verified with the consistency index (C-index), calibration curve, and decision curve analysis (DCA). Results: A total of 1778 ECCC patients, 991 from FIGO stage I/II and 787 from FIGO stage III/IV, were included in this study. The age at diagnosis, marital status, T stage, tumor size, and surgery-independent prognostic factors in FIGO stage I/II, and the age at diagnosis, T stage, lymph node involvement, distant metastasis, tumor size, surgery, radiotherapy, and chemotherapy in FIGO stage III/IV were independent prognostic factors. The C-indexes of the training and validation group were 0.766 and 0.697 for FIGO stage I/II and 0.721 and 0.708 for FIGO stage III/IV, respectively. The calibration curve revealed good agreement between nomogram-predicted and actual observation values. The DCA established that nomograms had better clinical benefits than the traditional FIGO stage. Conclusions: The predicted nomograms showed good accuracy, excellent discrimination ability, and clinical benefits, depicting their usage in clinical practice.

Clinical characteristics and prognosis of non-small cell lung cancer patients with liver metastasis: A population-based study.

BACKGROUND: The presence of liver metastasis (LM) is an independent prognostic factor for shorter survival in non-small cell lung cancer (NSCLC) patients. The median overall survival of patients with involvement of the liver is less than 5 mo. At present, identifying prognostic factors and constructing survival prediction nomogram for NSCLC patients with LM (NSCLC-LM) are highly desirable. AIM: To build a forecasting model to predict the survival time of NSCLC-LM patients. METHODS: Data on NSCLC-LM patients were collected from the Surveillance, Epidemiology, and End Results database between 2010 and 2018. Joinpoint analysis was used to estimate the incidence trend of NSCLC-LM. Kaplan-Meier curves were constructed to assess survival time. Cox regression was applied to select the independent prognostic predictors of cancer-specific survival (CSS). A nomogram was established and its prognostic performance was evaluated. RESULTS: The age-adjusted incidence of NSCLC-LM increased from 22.7 per 1000000 in 2010 to 25.2 in 2013, and then declined to 22.1 in 2018. According to the multivariable Cox regression analysis of the training set, age, marital status, sex, race, histological type, T stage, metastatic pattern, and whether the patient received chemotherapy or not were identified as independent prognostic factors for CSS (P < 0.05) and were further used to construct a nomogram. The C-indices of the training and validation sets were 0.726 and 0.722, respectively. The results of decision curve analyses (DCAs) and calibration curves showed that the nomogram was well-discriminated and had great clinical utility. CONCLUSION: We designed a nomogram model and further constructed a novel risk classification system based on easily accessible clinical factors which demonstrated excellent performance to predict the individual CSS of NSCLC-LM patients.

Analysis of risk factors and construction of a prediction model for short stature in children.

Background: Short stature in children is an important global health issue. This study aimed to analyze the risk factors associated with short stature and to construct a clinical prediction model and risk classification system for short stature. Methods: This cross-sectional study included 12,504 children aged 6-14 years of age from 13 primary and secondary schools in Pingshan District, Shenzhen. A physical examination was performed to measure the height and weight of the children. Questionnaires were used to obtain information about children and their parents, including sex, age, family environment, social environment, maternal conditions during pregnancy, birth and feeding, and lifestyle. The age confounding variable was adjusted through a 1 : 1 propensity score matching (PSM) analysis and 1,076 children were selected for risk factor analysis. Results: The prevalence of short stature in children aged 6-14 years was 4.3% in the Pingshan District, Shenzhen. The multivariate logistic regression model showed that the influencing factors for short stature were father's height, mother's height, annual family income, father's level of education and parents' concern for their children's height in the future (P < 0.05). Based on the short stature multivariate logistic regression model, a short stature nomogram prediction model was constructed. The area under the ROC curve (AUC) was 0.748, indicating a good degree of discrimination of the nomogram. According to the calibration curve, the Hosmer-Lemesio test value was 0.917, and the model was considered to be accurate. Based on a risk classification system derived from the nomogram prediction model, the total score of the nomogram was 127.5, which is considered the cutoff point to divides all children into low-risk and high-risk groups. Conclusion: This study analyzed the risk factors for short stature in children and constructed a nomogram prediction model and a risk classification system based on these risk factors, as well as providing short stature screening and assessment individually.

Suspected Malignant Thyroid Nodules in Children and Adolescents According to Ultrasound Elastography and Ultrasound-Based Risk Stratification Systems-Experience from One Center.

The risk of malignancy in thyroid nodules correlates with the presence of ultrasonographic features. In adults, ultrasound risk-classification systems have been proposed to indicate the need for further invasive diagnosis. Furthermore, elastography has been shown to support differential diagnosis of thyroid nodules. The purpose of our study was to assess the application of the American Thyroid Association (ATA), British Thyroid Association (BTA) ultrasound risk-classification systems and strain elastography in the management of thyroid nodules in children and adolescents from one center. Seventeen nodules with Bethesda III, IV, V and VI were selected from 165 focal lesions in children. All patients underwent ultrasonography and elastography followed by fine needle aspiration biopsy. Ultrasonographic features according to the ATA and BTA stratification systems were assessed retrospectively. The strain ratio in the group of thyroid nodules diagnosed as malignant was significantly higher than in benign nodules (6.07 vs. 3.09, p = 0.036). According to the ATA guidelines, 100% of malignant nodules were classified as high suspicion and 73% of benign nodules were assessed as low suspicion. Using the BTA U-score classification, 80% of malignant nodules were classified as cancerous (U5) and 20% as suspicious for malignancy (U4). Among benign nodules, 82% were classified as indeterminate or equivocal (U3) and 9% as benign (U2). Our results suggest that application of the ATA or BTA stratification system and elastography may be a suitable method for assessing the level of suspected malignancy in thyroid nodules in children and help make a clinical decision about the need for further invasive diagnosis of thyroid nodules in children.

A novel nomogram for predicting long-term heart-disease specific survival among older female primary breast cancer patients that underwent chemotherapy: A real-world data retrospective cohort study.

Background: The past decade has witnessed an improvement in survival rates for breast cancer, with significant inroads achieved in diagnosis and treatment approaches. Even though chemotherapy is effective for this patient population, cardiotoxicity remains a major challenge, especially in older people. It has been established that cardiovascular events are a major cause of death in older female primary breast cancer patients that underwent chemotherapy. In the present study, the independent prognostic factors were identified to develop a novel nomogram for predicting long-term heart disease-specific survival (HDSS) and improving patient management. Method: Older female primary breast cancer patients that underwent chemotherapy from 2010 to 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. HDSS was the primary endpoint of this study. Univariate and multivariate Cox regression analyses were conducted on the training cohort to identify independent prognostic factors of HDSS and construct a nomogram to predict the 5- and 8-year HDSS. The performance of the constructed nomogram was evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analyses. Finally, a risk classification system was constructed to assist in patient management. Result: A total of 16,340 patients were included in this study. Multivariate Cox regression analysis identified six independent prognostic factors: age, race, tumor stage, marital status, surgery, and radiotherapy. A nomogram based on these six factors yielded excellent performance, with areas under the curve of the ROC for 5- and 8-year HDSS of 0.759 and 0.727 in the training cohort and 0.718 and 0.747 in the validation cohort. Moreover, the established risk classification system could effectively identify patients at low-, middle-, and high- risk of heart disease-associated death and achieve targeted management. Conclusion: Independent prognostic factors of HDSS in older female primary breast cancer patients that underwent chemotherapy were determined in this study. A novel nomogram for predicting 5- and 8-year HDSS in this patient population was also established and validated to help physicians during clinical decision-making and screen high-risk patients to improve outcomes.

A Hot-Melt Extrusion Risk Assessment Classification System for Amorphous Solid Dispersion Formulation Development.

Several literature publications have described the potential application of active pharmaceutical ingredient (API)-polymer phase diagrams to identify appropriate temperature ranges for processing amorphous solid dispersion (ASD) formulations via the hot-melt extrusion (HME) technique. However, systematic investigations and reliable applications of the phase diagram as a risk assessment tool for HME are non-existent. Accordingly, within AbbVie, an HME risk classification system (HCS) based on API-polymer phase diagrams has been developed as a material-sparing tool for the early risk assessment of especially high melting temperature APIs, which are typically considered unsuitable for HME. The essence of the HCS is to provide an API risk categorization framework for the development of ASDs via the HME process. The proposed classification system is based on the recognition that the manufacture of crystal-free ASD using the HME process fundamentally depends on the ability of the melt temperature to reach the API's thermodynamic solubility temperature or above. Furthermore, we explored the API-polymer phase diagram as a simple tool for process design space selection pertaining to API or polymer thermal degradation regions and glass transition temperature-related dissolution kinetics limitations. Application of the HCS was demonstrated via HME experiments with two high melting temperature APIs, sulfamerazine and telmisartan, with the polymers Copovidone and Soluplus. Analysis of the resulting ASDs in terms of the residual crystallinity and degradation showed excellent agreement with the preassigned HCS class. Within AbbVie, the HCS concept has been successfully applied to more than 60 different APIs over the last 8 years as a robust validated risk assessment and quality-by-design (QbD) tool for the development of HME ASDs.

Adenosquamous Carcinoma of the Cervix: A Population-Based Analysis.

BACKGROUND: Due to the rarity of adenosquamous carcinoma of the cervix (ASCC), studies on the incidence, prognostic factors, and treatment outcomes of ASCC remain scarce. Therefore, we performed a retrospective population-based study to systematically investigate the characteristics of ASCC patients. METHODS: Patients with a histopathologically confirmed diagnosis of ASCC were enrolled from the Surveillance, Epidemiology, and End Results database between 1975 and 2016. Univariate and multivariate Cox regression analyses were performed to identify the potential predictors of cancer-specific survival (CSS) in patients with ASCC. Selected variables were integrated to establish a predictive nomogram and the predictive performance of the nomogram was estimated using Harrell's concordance index (C-index), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 1142 ASCC patients were identified and included in this study and were further randomized into the training and validation cohorts in a 7:3 ratio. The age-adjusted incidence of ASCC declined from 0.19 to 0.09 cases per 100,000 person-years between 2000 and 2017, with an annual percentage change of -4.05% (P<0.05). We identified age, tumor grade, FIGO stage, tumor size, and surgical procedure as independent predictors for CSS in ASCC patients and constructed a nomogram to predict the 3- and 5-year CSS using these prognostic factors. The calibration curve indicated an outstanding consistency between the nomogram prediction and actual observation in both the training and testing cohorts. The C-index was 0.7916 (95% CI: 0.7990-0.8042) and 0.8148 (95% CI: 0.7954-0.8342) for the training and testing cohorts, respectively, indicating an excellent discrimination ability of the nomogram. The DCA showed that the nomogram exhibited more clinical benefits than the FIGO staging system. CONCLUSIONS: We established and validated an accurate predictive nomogram for ASCC patients based on several clinical characteristics. This model might serve as a useful tool for clinicians to estimate the prognosis of ASCC patients.

A risk classification system predicting the cancer-specific survival for postoperative stage IB non-small-cell lung cancer patients without lymphovascular and visceral pleural invasion.

BACKGROUND: This study aims to formulate a risk classification system predicting the cancer-specific survival (CSS) for postoperative stage IB NSCLC patients without lymphovascular (LVI) and visceral pleural (VPI) invasion to guide treatment decision making and assist patient counseling. METHOD: A total of 4,238 patients were included in this study. Patients were randomly divided into training and validation cohorts (7:3). The risk factors were identified by Cox regression. Concordance index (C-index), calibration curves, and Decision Curve Analyses (DCAs) were used to evaluate the performance of nomogram. We applied X-tile to calculate the optimal cut-off points and develop a risk classification system. The Kaplan-Meier method was conducted to evaluate CSS in different risk groups, and the significance was evaluated by log-rank test. RESULT: Among the 4,238 patients, 1,014(23.9%) suffered cancer-specific death. In the training cohort, univariable and multivariable Cox regression analyses revealed that age, gender, pathological subtype, grade, tumor size, the number of removed lymph nodes and surgical type were significantly associated with CSS. According to these results, the nomogram was formulated. The C-index of the prediction model was 0.755 in the training cohort (95%CI: 0.733-0.777) and 0.726 (95%CI: 0.695-0.757) in the validation cohort. The calibration curves in training and validation cohort exhibited good agreement between the predictions and actual observations. The Decision Curve Analyses (DCAs) showed net benefit can be achieved for nomogram. A risk classification system was further constructed that could perfectly classify patients into three risk groups. CONCLUSION: In this study, we constructed a nomogram to support individualized evaluation of CSS and a risk classification system to identify patients in the different risk groups in stage IB NSCLC patients without LVI and VPI. These tools could be useful in guiding treatment decision making and assisting patient counseling.

Serum ferritin and ECOG performance status predict the response and improve the prognostic value of IPSS or IPSS-R in patients with high-risk myelodysplastic syndromes and oligoblastic acute myeloid leukemia treated with 5-azacytidine: a retrospective analysis of the Hellenic national registry of myelodysplastic and hypoplastic syndromes.

BACKGROUND: 5-azacytidine (5-AZA) improves survival of patients with higher-risk myelodysplastic syndromes (MDSs) and oligoblastic acute myeloid leukemia (AML); however, predictive factors for response and outcome have not been consistently studied. METHODS: This study of the Hellenic MDS Study Group included 687 consecutive patients with higher-risk MDS and oligoblastic AML treated with 5-AZA. RESULTS: The International Prognostic Scoring System (IPSS) revised version (IPSS-R), Eastern Cooperative Oncology Group Performance Status (ECOG PS) (0 or 1 versus ⩾2) and baseline serum ferritin (SF) levels > 520 ng/ml were shown to independently predict response to 5-AZA. In the survival analysis, the IPSS and IPSS-R risk classification systems along with the ECOG PS and SF levels > 520 ng/ml proved to be independent prognosticators for overall survival (OS), as well as for leukemia-free survival (LFS). Next, we built new multivariate models for OS and LFS, incorporating only ECOG PS and SF levels besides IPSS or IPSS-R risk classification systems. Thereby, the new modified IPSS and IPSS-R risk classification systems (H-PSS, H-PSS-R) could each discriminate a low, an intermediate and a high-risk patient group regarding OS and LFS. The H-PSS and H-PSS-R proved to be better predictors of OS than their previous counterparts as well as the French prognostic score, while the most powerful OS predictor was the new, H-PSS-R system. CONCLUSIONS: ECOG PS and SF levels > 520 ng/ml independently predict response to 5-AZA, OS and LFS. Their incorporation in the IPSS and IPSS-R scores enhances these scores' predictive power in 5-AZA-treated higher-risk MDS and oligoblastic AML patients.

Development of a risk grading system to identify patients with acute promyelocytic leukemia at high risk of early death.

BACKGROUND: Early death (ED) rate in acute promyelocytic leukemia (APL) remains high. Some studies have identified prognostic factors capable of predicting ED, whereas no risk rating system for ED has been reported in the literature. In this study, a risk classification system was built to identify subgroup at high risk of ED among patients with APL. METHODS: Totally, 364 consecutive APL patients who received arsenic trioxide as induction therapy were included. Ten baseline clinical characteristics were selected for analysis, and they were de novo/relapse, age, sex, white blood cell count, platelet count, serum fibrinogen, creatinine, uric acid, aspartate aminotransferase, and albumin. Using a training cohort (N=275), a multivariable logistic regression model was constructed, which was internally validated by the bootstrap method and externally validated using an independent cohort (N=89). Based on the model, a risk classification system was designed. Then, all patients were regrouped into de novo (N=285) and relapse (N=79) cohorts and the model and risk classification system were applied to both cohorts. RESULTS: The constructed model included 8 variables without platelet count and sex. The model had excellent discriminatory ability (optimism-corrected area under the receiver operator characteristic curve=0.816±0.028 in the training cohort and area under the receiver operator characteristic curve=0.798 in the independent cohort) and fit well for both the training and independent data sets (Hosmer-Lemeshow test, P=0.718 and 0.25, respectively). The optimism-corrected calibration slope was 0.817±0.12. The risk classification system could identify a subgroup comprising ~25% of patients at high risk of ED in both the training and independent cohorts (OR=0.140, P<0.001 and OR=0.224, P=0.027, respectively). The risk classification system could effectively identify patient subgroups at high risk of ED in not only de novo but also relapse cohorts (OR=0.233, P<0.001 and OR=0.105, P=0.001, respectively). CONCLUSION: All the results highlight the high practical value of the risk classification system.