Prognostic nomogram for adenoid cystic carcinoma in different anatomic sites

Estimating surgical probability: Development and validation of a prognostic model for patients with lumbar disc herniation treated with acupuncture

Lumbar disc herniation (LDH) is a common cause of pain in the lumbar spine and legs. While acupuncture has become the primary conservative treatment for LDH, some patients experience treatment failure and require surgery, causing substantial concern for clinicians. We developed an effective personalized clinical prediction model to identify the independent risk factors associated with acupuncture failure in patients with LDH. Our model aimed to predict the probability of surgery within 6 months of acupuncture failure in patients with LDH. A total of 738 patients with LDH who underwent acupuncture at 4 Chinese hospitals between January 2019 and October 2021 were selected. The patients were divided into training (n = 496) and validation (n = 242) cohorts. Seven predictive variables, including smoking, Oswestry Disability Index (ODI) score, lower-limb herniation, disc herniation type, lumbar spinal stenosis, lumbar lateral recess stenosis, and acupuncture frequency, were selected as risk factors using least absolute shrinkage and selection operato (LASSO) regression. A prediction model was developed using multivariate logistic regression analysis and a nomogram was constructed. The model exhibited good discrimination, with an area under the ROC curve (AUC) of 0.903 for the development cohort and 0.899 for the validation cohort. The Hosmer-Lemeshow goodness-of-fit test was a good fit for both cohorts (P = .956 for the development cohort; P = .513 for the validation cohort). Decision curve analysis (DCA) demonstrated that the threshold probabilities for the 2 cohorts ranged from > 4% and 5-95%, respectively. Therefore, the prediction model had a good net benefit. The nomogram established in this study, incorporating 7 risk factors, demonstrated a good predictive ability. It could predict acupuncture failure in LDH patients and the risk of surgery within 6 months, enabling physicians to conduct individualized treatment measures.

Epidemiology of and factors associated with overall survival for patients with head and neck adenoid cystic carcinoma

BACKGROUND

Updated epidemiologic and survival data of head and neck adenoid cystic carcinoma (HNACC) are lacking. This retrospective study aimed to clarify the incidence, prevalence, and overall survival (OS) of patients with HNACC and establish relevant nomogram.

METHODS

Trends in incidence, limited-duration prevalence, and relative survival (RS) rates were evaluated using data from the Surveillance, Epidemiology, and End Results (SEER) database, and annual percent change (APC) in rates was calculated using joinpoint regression. Data on age, sex, site, stage, and surgery were used in construction and validation of the nomogram.

RESULTS

The study included 6474 patients; 57.7% were female and 78.6% were white. The age-adjusted incidence rates of HNACC decreased significantly from 0.41 to 0.25 per 100,000 [1975-2018; average annual percent change (AAPC): - 1.37, P < 0.001], which was dominated by the localized stage. The 20-year limited duration prevalence increased from 0.00028% to 0.00262%. The 5- and 10-year RS rates of all HNACC patients were 80.0% and 65.5%, respectively. RS rates in HNACC showed a slight increase over time, with APC values of 0.03 for 5-year (P < 0.05) and 0.13 for 10-year (P < 0.05) RS. A prognostic model was constructed. The C-indices for the training and testing sets were both 0.734. The nomogram's discrimination efficiency was evaluated using the receiver operating characteristic curve and had moderate predictive power.

CONCLUSIONS

Over the past 40 years, the incidence of HNACC decreased accompanied by slightly improved survival rates. Nomogram was capable of predicting the 5- and 10-year OS rates with moderate accuracy.

Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage

Objectives

Inflammatory response biomarkers are promising prognostic factors to improve the prognosis of stroke-associated pneumonia (SAP) after ischemic stroke. This study aimed to investigate the prognostic significance of inflammatory response biomarkers on admission in SAP after spontaneous intracerebral hemorrhage (SICH) and establish a corresponding nomogram.

Methods

The data of 378 patients with SICH receiving conservative treatment from January 2019 to December 2021 at Taizhou People's Hospital were selected. All eligible patients were randomized into the training (70%, 265) and validation cohorts (30%, 113). In the training cohort, multivariate logistic regression analysis was used to establish an optimal nomogram, including inflammatory response biomarkers and clinical risk factors. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and performance, respectively. Moreover, this model was further validated in a validation cohort.

Results

A logistic regression analysis showed that intraventricular hemorrhage (IVH), hypertension, dysphagia, Glasgow Coma Scale (GCS), National Institute of Health Stroke Scale (NIHSS), systemic inflammation response index (SIRI), and platelet/lymphocyte ratio (PLR) were correlated with SAP after SICH (P < 0.05). The nomogram was composed of all these statistically significant factors. The inflammatory marker-based nomogram showed strong prognostic power compared with the conventional factors, with an AUC of 0.886 (95% CI: 0.841-0.921) and 0.848 (95% CI: 0.799-0.899). The calibration curves demonstrated good homogeneity between the predicted risks and the observed outcomes. In addition, the model has a significant net benefit for SAP, according to DCA. Also, internal validation demonstrated the reliability of the prediction nomogram. The length of hospital stay was shorter in the non-SAP group than in the SAP group. At the 3-month follow-up, clinical outcomes were worse in the SAP group (P < 0.001).

Conclusion

SIRI and PLR at admission can be utilized as prognostic inflammatory biomarkers in patients with SICH in the upper brain treated with SAP. A nomogram covering SIRI and PLR can more accurately predict SAP in patients' supratentorial SICH. SAP can influence the length of hospital stay and the clinical outcome.

High-risk subtype: Clinical manifestations and molecular characteristics of submandibular gland adenoid cystic carcinoma

Objective

Adenoid cystic carcinoma of the head and neck mainly occurs in the major salivary glands, of which the parotid gland and submandibular gland are the most common. The purpose of this study was to clarify the site-specific differences in prognosis and molecular expression characteristics of the patients and to achieve stratified risk management of the clinical prognosis.

Materials

By performing a single-centre retrospective analysis combined with analyses of the Surveillance, Epidemiology, and End Results (SEER) database, cBioPortal and GEO databases, the clinical prognostic characteristics and the differences in molecular expression patterns of ACC in the submandibular gland and parotid gland were analysed. Cox regression analysis, the chi-square test, Fisher's test and the log-rank test were used to compare the significance of differences.

Results

Compared with patients with parotid gland ACC, the submandibular gland ACC is more likely to have metastases in the cervical lymph node (21.7% vs. 3.3%) and shows a higher rate of distant metastasis within 1 year after the primary site diagnosis (47.8% vs. 23.3%), a worse overall prognosis, more frequent mutations of MYB/MYBL1 (50% vs. 25%) and abnormal upregulation of the phosphatidylinositol-3 kinase (PI3K) pathway.

Conclusions

Submandibular gland ACC is associated with site-specific early cervical lymph node metastasis and hidden distant metastasis, along with rapid progression and a poor prognosis. A high MYB/MYBL1 mutation rate and abnormal upregulation of the PI3K pathway with MYB involvement were identified.

Occult neck metastases risk factors and the role of elective neck dissection in cT3-4N0 adenoid cystic carcinoma of the parotid gland

Objectives

To determine the predictor for occult neck metastases and the role of elective neck dissection (END) in cT3-4N0 parotid adenoid cystic carcinoma (ACC).

Methods

Patients with surgically treated parotid ACC were retrospectively enrolled. Predictors of occult neck metastases and the effect of END on disease specific survival (DSS), overall survival (OS), locoregional control survival (LRC), and distant metastasis free survival (DMS) were analyzed.

Results

Occult neck metastases occurred in 35 (19.7%) of the 178 patients undergoing an END. The tumor stage [p=0.011, 4.215 (1.387–10.435)] and intra-parotid lymph node metastasis [p=0.032, 3.671 (1.693–8.775)] were related to the possibility of occult neck metastases independently. The END group had better 10-year LRC than the observation group (56% vs. 43%, p=0.002) and also better 10-year DMS than the observation group (43% vs. 32%, p<0.001). The two groups had similar 10-year DSS (40% vs. 33%, p=0.230) and OS (31% vs. 23%, p=0.094) rates. Furthermore, the Cox model confirmed that END was independently associated with better LRC rate [p=0.022, 2.576 (1.338–6.476)] and better DMS [p=0.011, 2.343 (1.274–7.462)].

Conclusions

Occult neck metastases in cT3-4N0 parotid ACC was not common. A T4 tumor with intra-parotid lymph node metastasis had the highest possibility of occult neck metastases. END had no effect on DSS or OS but significantly decreased the risk of locoregional recurrence and distant metastasis.

Clinical Prediction Nomograms to Assess Overall Survival and Disease-Specific Survival of Patients with Salivary Gland Adenoid Cystic Carcinoma

Aim

Salivary gland adenoid cystic carcinoma (SACC) is the second highest incidence of malignant salivary gland tumor. The purpose of this study was to establish nomograms combined with SACC patients based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

Patients with SACC were included in the SEER∗Stat Database from 2004 to 2016. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to filter potential prognostic clinical variables. Multivariate analysis from the Cox proportional hazards model was performed to determine the independent prognostic factors on overall survival (OS) and disease-specific survival (DSS), applied to develop nomograms. The Schönfeld residual test verified the proportional hazard assumption. The discrimination and consistency of nomograms was assessed and validated according to concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration curves using an internal 1,000 times bootstrap resampling. The nomogram's net clinical benefit was assessed through decision curve analysis (DCA).

Results

A total of 658 patients with SACC were included. Age, T stage, N stage, M stage, histologic grade, and surgery were independent prognostic factors for OS and DSS. Based on these independent prognostic factors, nomograms were developed to predict 3-, 5-, and 10-year OS and DSS. In the validation of 1,000 times bootstrap resampling, the C-index and ROC curves had good discriminatory ability. The calibration curves indicated excellent consistency between the predicted and actual survival results in the nomograms. The DCA curves demonstrated that the nomograms had good clinical benefit and were superior to the TNM stage and other variables.

Conclusions

Two nomograms developed in this study precisely predicted the 3-, 5-, and 10-year OS and DSS rates of patients with SACC in accordance with independent prognostic factors, and their clinical value is better than TNM staging, providing a prognostic reference for other SACC patients.

A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study

BACKGROUND

Neonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS.

METHODS

A prediction model was built including 243 late-preterm and full-term infants from Daping Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and Dec 31, 2019. 80 patients from the Children's Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and June 30, 2018 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of neonatal ARDS. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples.

FINDINGS

Multivariate logistic regression demonstrated that mother's education level (odds ratio [OR] 0·478, 95% confidence interval [CI] 0·324-0·704), premature rupture of membrane (OR 0·296, 95% CI 0·133-0·655), infectious disease within 7 days before delivery (OR 0·275, 95% CI 0·083-0·909), hospital level (OR 2·479, 95% CI 1·260-4·877), and Apgar 5-min score (OR 0·717, 95% CI 0·563-0·913) were independent predictors for neonatal ARDS in late-preterm and full-term infants, who experienced dyspnoea within 24 h after birth and required mechanical ventilation. The area under the curve and concordance index of the nomogram constructed from the above five factors were 0·760 and 0·757, respectively. The Hosmer-Lemeshow test showed that the model was a good fit (P = 0.320). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram.

INTERPRETATION

A nomogram based on perinatal factors was developed to predict the occurrence of neonatal ARDS in late-preterm and full-term infants who experienced dyspnoea within 24 h after birth and required mechanical ventilation. It provided clinicians with an accurate and effective tool for the early prediction and timely management of neonatal ARDS.

FUNDING

No funding was associated with this study.

Development and validation of a nomogram for prognosis of sinonasal adenocarcinoma (a nomogram for sinonasal adenocarcinoma)

BACKGROUND

The incidence of sinonasal adenocarcinoma is low, and there are few studies on survival and prognosis. Therefore, we aim to develop and validate a prognostic model for predicting the overall survival of sinonasal adenocarcinoma and provide guidance for clinical management.

METHODS

Patients who were diagnosed as sinonasal adenocarcinoma through Surveillance, Epidemiology, and End Results database between 1975 and 2015 were randomly divided into a training group and validation group. Univariate, multivariate survival analysis was performed to screen independent survival factors. A nomogram was established to predict the overall survival rate of sinonasal adenocarcinoma. Receiver operating characteristic curve and calibration plot were performed to verify the discrimination and accuracy of the model. A decision curve analysis was performed to verify the clinical applicability of the model.

RESULTS

A total of 423 patients with sinonasal adenocarcinoma were randomly divided into training group (n = 299) and verification group (n = 124). We established and verified the Nomo map including age, marriage, grade, surgery and tumour size. The c-index of Surveillance, Epidemiology, and End Results stage, T stage and this model are 0.635, 0.626 and 0.803, respectively. The survival rate of the high-risk group scored by this model was lower than that of the low-risk group (P < 0.001). Decision curve analysis shows that the model has advantages in predicting survival rates.

CONCLUSION

Our model is considered to be a useful tool for predicting the overall survival of sinonasal adenocarcinoma, with good discrimination and clinical applicability. We hope that this model will help rhinologists to make clinical decisions and manage patients diagnosed with sinonasal adenocarcinoma.

Adenoid cystic carcinoma. An indolent but aggressive tumour. Part A: from aetiopathogenesis to diagnosis

Adenoid cystic carcinoma (ACC) is a relatively rare tumour of the minor and major salivary glands. It is uncommon in the parotid gland while it is much more frequent in the submandibular gland and in minor salivary and mucinous glands (oral cavity, oropharynx, and paranasal sinuses). ACC may also arise in secretory glands located in other tissues, such as in the tracheobronchial tree, oesophagus, breast, lungs, prostate, uterine cervix, lachrymal and Bartholin’s glands, and skin. The natural history of ACC is characterised by an indolent growth rate, a relatively low probability of regional lymph node metastases and a high likelihood of haematogenous dissemination. ACC has been traditionally subdivided into three histological groups (cribriform, tubular, and solid) based on solid components of the tumour. Some studies have shown that tumours with a solid growth component have a rapid fatal course, compared to tumours without a solid growth component, but other studies have failed to correlate growth patterns with clinical course. The purpose of this review is to analyse the very large number of studies (sometimes contradictory) on ACC. In this first part, the aetiology, epidemiology, histopathology, clinical behaviour and diagnostic workup are examined.

Establishment and validation of a novel nomogram to predict overall survival in nasopharyngeal carcinoma with lymph node metastasis

BACKGROUND

The aim of the present study was to establish and validate a nomogram to predict the overall survival (OS) in nasopharyngeal carcinoma (NPC) patients with lymph node metastasis (LNM).

METHODS

A novel nomogram was constructed using 863 patients with LNM-positive NPC from the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors in the nomograms were determined using multivariate Cox risk analysis. The predictive capability was evaluated using calibration curves and decision curve analysis (DCA).

RESULTS

Multivariate analysis identified seven factors that could be used to construct the nomogram: age, pathological type, T stage, M stage, surgery of primary site, radiotherapy, and chemotherapy. The calibration curves and DCA demonstrated optimal agreement. Based on the nomogram, all patients could be stratified into three risk groups: low, middle, and high.

CONCLUSIONS

The novel nomogram demonstrated its potential as an individualized tool to predict OS.