Dynamic prediction of cancer-specific survival for primary hypopharyngeal squamous cell carcinoma

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic colorectal cancer

AIMS

The aim of this study is to enhance the accuracy of monitoring and treatment information for patients diagnosed with colorectal cancer (CRC).

METHODS

Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, a cohort of 335,948 eligible CRC patients was included in this investigation. Conditional survival probability and actuarial overall survival were employed as methodologies to investigate the association between clinicopathological characteristics and cancer prognosis.

RESULTS

Among CRC patients, the 5-year survival rate was 59%, while the 10-year survival rate was 42%. Over time, conditional survival showed a consistent increase, with rates reaching 45% and 48% for individuals surviving 1 and 2 years, respectively. Notably, patients with unfavorable tumor stages exhibited substantial improvements in conditional survival, thereby narrowing the disparity with actuarial overall survival over time.

CONCLUSION

This study underscores the significance of time-dependent conditional survival probability, particularly for patients with a poorer prognosis. The findings suggest that long-term CRC survivors may experience improved cancer prognosis over time.

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning

OBJECTIVES

To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI.

METHODS

MR images were collected from 222 HPC patients, among them 178 patients were used for training, and another 44 patients were recruited for testing. U-Net and DeepLab V3 + architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC).

RESULTS

The predicted tumor volumes by DeepLab V3 + model and U-Net model were highly correlated with those delineated manually (p < 0.001). The DSC of DeepLab V3 + model was significantly higher than that of U-Net model (0.77 vs 0.75, p < 0.05), particularly in those small tumor volumes of < 10 cm3 (0.74 vs 0.70, p < 0.001). For radiomics extraction of the first-order features, both models exhibited high agreement (ICC: 0.71-0.91) with manual delineation. The radiomics extracted by DeepLab V3 + model had significantly higher ICCs than those extracted by U-Net model for 7 of 19 first-order features and for 8 of 17 shape-based features (p < 0.05).

CONCLUSION

Both DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images, whereas DeepLab V3 + had a better performance than U-Net.

CLINICAL RELEVANCE STATEMENT

The deep learning model, DeepLab V3 + , exhibited promising performance in automated tumor segmentation and radiomics extraction for hypopharyngeal cancer on MRI. This approach holds great potential for enhancing the radiotherapy workflow and facilitating prediction of treatment outcomes.

KEY POINTS

• DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images. • DeepLab V3 + model was more accurate than U-Net in automated segmentation, especially on small tumors. • DeepLab V3 + exhibited higher agreement for about half of the first-order and shape-based radiomics features than U-Net.

Dynamic nomogram for long-term survival in patients with locally advanced oropharyngeal cancer after (chemo)radiotherapy

PURPOSE

This study aimed to establish a nomogram for predicting overall survival (OS) in oropharyngeal cancer patients treated with curative (chemo)radiotherapy.

MATERIALS AND METHODS

The dynamic nomogram was constructed on 273 patients with oropharyngeal squamous cell carcinoma treated in a Tertiary Head and Neck Cancer Unit. The clinical features that were previously reported to be associated with OS were analyzed. The performance of the nomogram was assessed using concordance index (C-index) and calibration curves.

RESULTS

The nomogram incorporated three explanatory variables derived from a decision tree approach including HPV status, N classification according to 8th edition TNM and early response to (chemo)radiotherapy. The nomogram was capable to predict OS with a validation C-index of 0.768. The proposed stratification in risk groups allowed significant distinction between Kaplan-Meier curves for OS outcome (p < 0.0001).

CONCLUSIONS

The nomogram provided an accurate evaluation of OS for oropharyngeal cancer patients treated with curative (chemo)radiotherapy.

Updated racial disparities in incidence, clinicopathological features and prognosis of hypopharyngeal squamous carcinoma in the United States

OBJECTIVE

This study was to determine the racial disparities in incidence, clinicopathological features and prognosis of hypopharyngeal squamous cell carcinoma (HPSCC) in the US.

METHODS

The National Program of Cancer Registries and Surveillance, Epidemiology, and End Results (SEER) database was used to determine racial disparity in age adjusted incidence rate (AAIR) of HPSCC and its temporal trend during 2004-2019. Using the separate SEER 17 database, we further evaluated racial disparity in clinicopathological features, and in prognosis using Kaplan-Meier curves and Cox proportional hazard models.

RESULTS

HPSCC accounted for 95.8% of all hypopharyngeal cancers and occurred much more frequently in males. Its incidence decreased in both male and females, in male non-Hispanic white (NHW), non-Hispanic black (NHB) and Hispanic as well as female NHW and NHB during the study period. NHB had the highest, whereas non-Hispanic Asian or Pacific Islanders (API) had comparable and the lowest incidence in both males and females. Among 6,172 HPSCC patients obtained from SEER 17 database, 80.6% were males and 83.9% were at the advanced stages III/IV. Five-year cancer specific and overall survival rates were 41.2% and 28.9%, respectively. NHB patients were more likely to be younger, unmarried, from the Southern region, larger sized tumor, and at the stage IV, but less likely to receive surgery. They also had higher proportions of dying from HPSCC and all causes. Multivariate analyses revealed that NHB with HPSCC at the locally advanced stage had both significantly worse cancer specific and overall survival compared with NHW, but not at early stage (I/II) or distant metastatic stage. Hispanic patients had significantly better prognosis than NHW at locally advanced and metastatic stages. NHW and API had comparable prognoses.

CONCLUSIONS

HPSCC displays continuously decreased incidence and racial disparity. The majority of the disease is diagnosed at the advanced stage. NHB have the highest burden of HPSCC and a worse prognosis. More studies are needed to curtail racial disparity and improve early detection.

Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma

Purpose

To propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC.

Materials and methods

Clinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models.

Results

PCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC₁ > 0.7 and ICC₂ > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components.

Conclusion

This study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy.

Prognostic Nomogram for Postoperative Hypopharyngeal Squamous Cell Carcinoma to Assist Decision Making for Adjuvant Chemotherapy

We aimed to investigate the effect of lymph node parameters on postoperative hypopharyngeal squamous cell carcinoma (HSCC) and to establish a nomogram to predict its prognosis and assist in adjuvant chemotherapy decisions. A retrospective analysis of postoperative HSCC in the Surveillance, Epidemiology, and End Results database (2004-2019) was performed. Cutoff points for continuous variables were determined by X-tile software. Univariate and multivariate analyses were performed to identify prognostic factors on overall survival (OS), and these variables were used to construct a nomogram. The nomogram's accuracy was internally validated using concordance index, area under the curve, calibration plot, and decision curve analyses. Furthermore, the value of chemotherapy in each risk subgroup was assessed separately based on individualized scores from the nomogram. In total, 404 patients were eligible for analysis, and the median OS was 39 months. Age, origin, primary site, T stage, number of lymph nodes examined, lymph node ratio, and radiotherapy were identified as prognostic factors for OS and incorporated into the nomogram. In both the training and validation cohorts, favorable performance was exhibited compared with the other stage systems, and patients could be classified into low-, intermediate-, and high-risk subgroups. Chemotherapy significantly improved the OS in the high-risk subgroup, whereas chemotherapy did not confer a survival benefit in the low- or intermediate-risk groups. The lymph node parameter-based nomogram model can better stratify the prognosis of HSCC patients and screen out patients who would benefit from chemotherapy, suggesting that the model could be used as a reference for clinical decision making and to avoid overtreatment.

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Hypopharyngeal Squamous Cell Carcinomas: A Population-Based Study

Background: Hypopharyngeal squamous cell carcinomas (HPSCC) is one of the causes of death in elderly patients, an accurate prediction of survival can effectively improve the prognosis of patients. However, there is no accurate assessment of the survival prognosis of elderly patients with HPSCC. The purpose of this study is to establish a nomogram to predict the cancer-specific survival (CSS) of elderly patients with HPSCC.

Methods: The clinicopathological data of all patients from 2004 to 2018 were downloaded from the SEER database. These patients were randomly divided into a training set (70%) and a validation set (30%). The univariate and multivariate Cox regression analysis confirmed independent risk factors for the prognosis of elderly patients with HPSCC. A new nomogram was constructed to predict 1-, 3-, and 5-year CSS in elderly patients with HPSCC. Then used the consistency index (C-index), the calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical value of the model.

Results: A total of 3,172 patients were included in the study, and they were randomly divided into a training set (N = 2,219) and a validation set (N = 953). Univariate and multivariate analysis suggested that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage were independent risk factors for patient prognosis. These nine variables are included in the nomogram to predict the CSS of patients. The C-index for the training set and validation was 0.713 (95% CI, 0.697–0.729) and 0.703 (95% CI, 0.678–0.729), respectively. The AUC results of the training and validation set indicate that this nomogram has good accuracy. The calibration curve indicates that the observed and predicted values are highly consistent. DCA indicated that the nomogram has a better clinical application value than the traditional TNM staging system.

Conclusion: This study identified risk factors for survival in elderly patients with HPSCC. We found that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage are independent prognostic factors. A new nomogram for predicting the CSS of elderly HPSCC patients was established. This model has good clinical application value and can help patients and doctors make clinical decisions.

Development and validation of a nomogram to predict cancer-specific survival in patients with hypopharyngeal squamous cell carcinoma treated with primary surgery

OBJECTIVE

We aimed to develop a nomogram to predict cancer-specific survival (CSS) in patients with hypopharyngeal squamous cell carcinoma (HSCC) treated with primary surgery to provide more accurate risk stratification for patients.

METHODS

We retrospectively collected data of 1144 eligible patients with HSCC from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Patients were randomly divided into training and validation groups (ratio 6:4) and we used univariate and multivariate Cox analysis. We developed and validated a nomogram using calibration plots and time-dependent receiver operating characteristic, Kaplan-Meier, and decision curves.

RESULTS

Age; marital status; T, N, and M stage; and postoperative adjuvant therapy were independent factors associated with CSS, which were included in the nomogram. The nomogram's C-index was 0.705 to 0.723 in the training group and 0.681 to 0.736 in the validation group, which were significantly higher than conventional American Joint Committee on Cancer (AJCC) staging. Calibration curves showed good agreement between prediction and observation in both groups. Kaplan-Meier and decision curves suggested the nomogram had better risk stratification and net benefit than conventional AJCC staging.

CONCLUSIONS

We established a nomogram that was superior to conventional AJCC staging in predicting CSS for HSCC.

Nomograms predicting prognosis for locally advanced hypopharyngeal squamous cell carcinoma

PURPOSE

This study aimed to construct nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) for patients with locally advanced hypopharyngeal squamous cell carcinoma (HSCC).

METHODS

864 patients with locally advanced HSCC during 2010-2015 from the surveillance, epidemiology and end results (SEER) database were selected. After classifying continuous data by risk, Cox regression analyses were applied to detect significant independent prognostic factors, with which nomograms were established. To evaluate the value of nomograms, concordance index (C-index), area under the receiver-operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA), Kaplan-Meier analysis was adopted. The efficacy of surgery in different risk groups was also studied to figure out people who can benefit from surgery.

RESULTS

A total of 864 locally advanced HSCC patients were randomized into the training cohort (n = 608) and the validation cohort (n = 256). Age, race, tumor size, T stage, N stage, primary site, radiotherapy, and chemotherapy were independent prognostic factors for OS and CSS (except race) and formed the nomograms. The nomograms revealed satisfied performance in C-index, AUC, DCA, and calibration curves, and prevailed over American Joint Committee on Cancer (AJCC) TNM staging system in predicting OS and CSS. After risk stratification, patients of low-risk group resulted in the best outcomes. Patients in moderate-risk may benefit from surgery.

CONCLUSIONS

Convenient and well-calibrated nomograms to predict OS and CSS for III/IVA/IVB-stage HSCC patients were set up and assessed and may do a favor to make clinical decisions.

Development and Validation of a Dynamic Nomogram to Predict the Risk of Neonatal White Matter Damage

Purpose

White matter damage (WMD) was defined as the appearance of rough and uneven echo enhancement in the white matter around the ventricle. The aim of this study was to develop and validate a risk prediction model for neonatal WMD.

Materials and Methods

We collected data for 1,733 infants hospitalized at the Department of Neonatology at The First Affiliated Hospital of Zhengzhou University from 2017 to 2020. Infants were randomly assigned to training (n = 1,216) or validation (n = 517) cohorts at a ratio of 7:3. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model and web-based risk calculator based on the training cohort data. The predictive accuracy of the model was verified in the validation cohort.

Results

We identified four variables as independent risk factors for brain WMD in neonates by multivariate logistic regression and LASSO analysis, including gestational age, fetal distress, prelabor rupture of membranes, and use of corticosteroids. These were used to establish a risk prediction nomogram and web-based calculator (https://caowenjun.shinyapps.io/dynnomapp/). The C-index of the training and validation sets was 0.898 (95% confidence interval: 0.8745-0.9215) and 0.887 (95% confidence interval: 0.8478-0.9262), respectively. Decision tree analysis showed that the model was highly effective in the threshold range of 1-61%. The sensitivity and specificity of the model were 82.5 and 81.7%, respectively, and the cutoff value was 0.099.

Conclusion

This is the first study describing the use of a nomogram and web-based calculator to predict the risk of WMD in neonates. The web-based calculator increases the applicability of the predictive model and is a convenient tool for doctors at primary hospitals and outpatient clinics, family doctors, and even parents to identify high-risk births early on and implementing appropriate interventions while avoiding excessive treatment of low-risk patients.

Risk stratification of postoperative recurrence in hypopharyngeal squamous-cell carcinoma patients with nodal metastasis

PURPOSE

To explore lymph node-related risk factors and investigate the benefit of different adjuvant therapy strategies in hypopharyngeal squamous-cell carcinoma (HPSCC) patients with nodal metastasis (N +).

METHODS

We conducted a retrospective review covering 266 HPSCC patients with nodal metastasis. Kaplan-Meier curves and Cox proportional hazard models were utilized to evaluate recurrence-free survival (RFS) and independent risk factors.

RESULTS

pT3-T4, extranodal extension, lymphovascular invasion, and lower lymph node involvement were high-risk factors leading to poorer RFS in N + HPSCC patients. Patients were classified into three groups based on the recursive-partitioning analysis (RPA). Postoperative chemoradiation significantly improved RFS in patients in the high-risk group (p < 0.001). For patients in the low- and intermediate-risk groups, the application of adjuvant therapies showed no significant benefit on RFS (p = 0.74 and 0.53, respectively).

CONCLUSIONS

The novel risk stratification for N + HPSCC patients can predict the risk of postoperative recurrence effectively. Adjuvant chemoradiation is preferred for patients in the high-risk group as it lowers risk of recurrence. Conversely, for patients in the low- and intermediate-risk groups, regular observation and follow-up strategies are a valid form of treatment.