Radiotherapy for patients with esophageal cancer aged 80 years or older: A 16-year experience.

PURPOSE: Radiotherapy (RT) plays an important role in esophageal cancer (EC) patients aged ≥80 years. However, the survival modality and prognostic factors remain poorly understood. Thus, this study aimed to evaluate the tolerance and long-term overall survival (OS) of patients aged ≥80 years who were diagnosed with EC and underwent definitive RT. MATERIALS AND METHODS: A total of 213 consecutive patients with EC over 80 years old who were treated with curative intent RT between February 1999 and December 2015 at our institution were retrospectively reviewed. The clinical prognostic variables were analyzed against OS in univariate analyses using log-rank tests and in a multivariate model using Cox regression proportional hazards analysis. RESULT: The median patient age was 82 (range: 80-94) years. Atotal of 192 patients (90.1%) completed the definitive RT (median: 60 Gy, range: 50-72 Gy), and 11 patients had grade 4 or higher acute toxicity, including esophagitis, a cardiac event, infections, and sudden death. Atotal of 168 deaths (78.9%) were observed with a median follow up of 47 months (range: 0-153 months). The OS rates were 50.3%, 17.6%, and 13.2% at 1, 3, and 5 years, respectively. Multivariable analysis identified that tumors located in the cervical and upper thorax, a shorter tumor lesion, RT treatment of 50-60Gy, and a better response to treatment were the factors associated with longer OS. CONCLUSION: Definitive RT could be considered as an effective treatment for patients with EC who are older than 80 years, and 50-60 Gy seems to be a reasonable dose for these patients.

FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy.

BACKGROUND AND OBJECTIVE: Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. METHODS: In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. RESULT: The experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. CONCLUSION: The experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients.

OBJECTIVES: To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy. METHODS: LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed. RESULTS: Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018). CONCLUSION: Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT: Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS: • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.

CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma.

BACKGROUND: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches. PURPOSE: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT). METHODS: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans. RESULTS: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. CONCLUSION: MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.

Development and Validation of a Questionnaire to Measure ICU Nurses' Knowledge, Attitudes, and Practices Related to Central Line Bloodstream Infection Practices.

BACKGROUND: Currently, there is no instrument available to assess intensive care unit (ICU) nurses' knowledge, attitudes, and practices (KAP) of central line-associated bloodstream infection (CLABSI) prevention practices. PURPOSE: To develop and validate a CLABSI questionnaire to measure ICU nurses' KAP (CLABSI-KAP-Q). METHODS: Data were collected from 255 nurses at 4 hospitals in Gansu Province, China. Questions on the CLABSI-KAP-Q were generated through a review of the literature, interviews with nurses, and multiple rounds of content validity evaluation by experts. The validity and reliability of the CLABSI-KAP-Q were assessed with exploratory factor analysis, confirmatory factor analysis, internal consistency, and correlation coefficients. RESULTS: The final version of the CLABSI-KAP-Q consisted of 32 items. The reliability was represented by a Cronbach α of 0.946, while the test-retest reliability was 0.945. The overall content validity was 0.95. CONCLUSIONS: The CLABSI-KAP-Q is shown to be valid and reliable and recommended for use in clinical practice.

Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics.

OBJECTIVE: This study aimed to construct a new staging system for patients with esophageal squamous cell carcinoma (ESCC) based on combined pathological TNM (pTNM) stage, radiomics, and proteomics. METHODS: This study collected patients with radiomics and pTNM stage (Cohort 1, n = 786), among whom 103 patients also had proteomic data (Cohort 2, n = 103). The Cox regression model with the least absolute shrinkage and selection operator, and the Cox proportional hazards model were used to construct a nomogram and predictive models. Concordance index (C-index) and the integrated area under the time-dependent receiver operating characteristic (ROC) curve (IAUC) were used to evaluate the predictive models. The corresponding staging systems were further assessed using Kaplan-Meier survival curves. RESULTS: For Cohort 1, the RadpTNM4c staging systems, constructed based on combined pTNM stage and radiomic features, outperformed the pTNM4c stage in both the training dataset 1 (Train1; IAUC 0.711 vs. 0.706, p < 0.001) and the validation dataset 1 (Valid1; IAUC 0.695 vs. 0.659, p < 0.001; C-index 0.703 vs. 0.674, p = 0.029). For Cohort 2, the ProtRadpTNM2c staging system, constructed based on combined pTNM stage, radiomics, and proteomics, outperformed the pTNM2c stage in both the Train2 (IAUC 0.777 vs. 0.610, p < 0.001; C-index 0.898 vs. 0.608, p < 0.001) and Valid2 (IAUC 0.746 vs. 0.608, p < 0.001; C-index 0.889 vs. 0.641, p = 0.009) datasets. CONCLUSIONS: The ProtRadpTNM2c staging system, based on combined pTNM stage, radiomic, and proteomic features, improves the predictive performance of the classical pTNM staging system.

Comparison of survival, acute toxicities, and dose-volume parameters between intensity-modulated radiotherapy with or without internal target volume delineation method and three-dimensional conformal radiotherapy in cervical cancer patients: A retrospective and propensity score-matched analysis.

BACKGROUND: To evaluate whether the use of the internal target volume (ITV) delineation method improves the performance of intensity-modulated radiotherapy (IMRT) and three-dimensional conformal radiotherapy (3DCRT) in terms of survival, acute toxicities, and dose-volume parameters. METHODS: A total number of 477 cervical cancer patients who received concurrent chemoradiotherapy (CCRT) from January 2012 to December 2016 were retrospectively analyzed. They were divided into four groups: the non-ITV (N-ITV) + IMRT, ITV + IMRT, N-ITV + 3DCRT, and ITV + 3DCRT groups, with 76, 41, 327, and 33 patients, respectively. Survival analysis was performed with the Kaplan-Meier and the log-rank tests, and acute toxicity analysis was performed with the chi-squared test and the binary logistic regression test. Using the propensity score matching (PSM) method, 92 patients were matched among the four groups, and their dose-volume parameters were assessed with the Kruskal-Wallis method. RESULTS: The median follow-up time was 49 months (1-119) for overall survival (OS). The 5-year OS rate was 66.4%. The ITV delineation method was an independent prognostic factor for OS (HR [95% CI]: 0.52 [0.27, 0.98], p = 0.044) and progression-free survival (PFS) (HR [95% CI]: 0.59 [0.36, 0.99], p = 0.045). The ITV + IMRT group had the lowest incidence rate (22%) and the N-ITV + IMRT group had the highest incidence rate of grade ≥3 hematological toxicity (HT) (46.1%) among the four groups. The pelvic bone marrow relative V10, V20, and V30 in the N-ITV + IMRT group was higher than those in the ITV + IMRT and N-ITV + 3DCRT groups (p < 0.05). CONCLUSIONS: The use of ITV for IMRT treatment planning was associated with improved overall survival and progression-free survival, with lower HT rate.

Late Toxicities, Failure Patterns, Local Tumor Control, and Survival of Esophageal Squamous Cell Carcinoma Patients After Chemoradiotherapy With a Simultaneous Integrated Boost: A 5-Year Phase II Study.

PURPOSE: We aimed to evaluate the long-term outcomes of concurrent chemoradiotherapy (CCRT) with a simultaneous integrated boost (SIB) of radiotherapy for esophageal squamous cell carcinoma (ESCC). METHODS AND MATERIALS: Eighty-seven patients with primary ESCC enrolled in this phase II trial. The majority (92.0%) had locoregionally advanced disease. They underwent definitive chemoradiotherapy. The radiotherapy doses were 66 Gy for the gross tumor and 54 Gy for the subclinical disease. Doses were simultaneously administered in 30 fractions over 6 weeks. The patients also underwent concurrent and adjuvant chemotherapy, which comprised cisplatin and fluorouracil. The study end points were acute and late toxicities, first site of failure, locoregional tumor control, and overall survival rates. RESULTS: The median follow-up time was 65.7 (range, 2.2-97.5) months for all patients and 81.5 (range, 19.4-97.5) months for those alive. There were 17 cases (19.5%) of severe late toxicities, including four cases (4.6%) of grade 5 and seven (8.0%) of grade 3 esophageal ulceration, four (4.6%) of grade 3 esophageal stricture, and two (2.3%) of grade 3 radiation-induced pneumonia. Twenty-three (26.4%) patients had locoregional disease progression. Most (86.7%) locally progressive lesions were within the dose-escalation region in the initial radiation plan, while majority of the recurrent lymph nodes were found out-of-field (83.3%) and in the supraclavicular region (75.0%). The 1-, 2-, 3-, and 5-year locoregional tumor control and overall survival rates were 79.2%, 72.4%, 72.4%, 70.8%, and 82.8%, 66.6%, 61.9%, 58.4%, respectively. Incomplete tumor response, which was assessed immediately after CCRT was an independent risk predictor of disease progression and death in ESCC patients. CONCLUSIONS: CCRT with SIB was well tolerated in ESCC patients during treatment and long-term follow-up. Moreover, patients who underwent CCRT with SIB exhibited improved local tumor control and had better survival outcomes compared to historical data of those who had standard-dose radiotherapy.

Distribution of Peripheral Blood Cells in Esophageal Cancer Patients During Concurrent Chemoradiotherapy Predicts Long-Term Locoregional Progression Hazard After Treatment (GASTO1072).

PURPOSE: Predicting the response to chemoradiotherapy is critical for the optimal management of esophageal cancer; however, it remains an unmet clinical need. This study aimed to evaluate the predictive potential of peri-treatment peripheral blood cells (PBC) in disease progression hazard in esophageal cancer following chemoradiotherapy. PATIENTS AND METHODS: A total of 87 patients with primary esophageal squamous cell carcinoma were subjected to definitive concurrent chemoradiotherapy in a Phase II trial. PBC parameters (hemoglobin, neutrophils, platelets, lymphocytes, and monocytes) were collected at seven time points throughout the course of radiotherapy. The potential of peri-treatment PBC parameters to predict the 3-year cumulative hazard of tumor progression was evaluated. RESULTS: Patients with disease progression displayed distinct distribution patterns of peri-treatment PBC compared to that in patients without disease progression. Greater prediction capabilities for risk of locoregional disease progression were found in PBC collected after the start of radiotherapy compared to those in their pretreatment counterparts, and in individual parameters rather than cell-to-cell ratios. The most predictive PBC parameters were integrated by summation and designated as a PBC score (PBCS), which further augmented their predictive power. Patients classified according to their PBCS (high vs medium v. low) had significantly different 3-year cumulative hazards of locoregional progression (58% vs 29% vs 7%, P = 0.0017). Multivariate analysis confirmed that high PBCS (HR, 12.2; 95% CI, 2.0-76.3; P = 0.007) and medium (HR, 5.8; 95% CI 1.2-27.7; P = 0.028) were independent indicators of locoregional progression. CONCLUSION: Systematic analysis of PBC distribution in esophageal cancer patients undergoing definitive chemoradiotherapy could help predict long-term locoregional progression hazard after treatment.

Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy.

BACKGROUND: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT). METHODS: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n = 99) or the validation cohort (n = 55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score, was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis, which was equal to the log-partial hazard of the Cox model in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms. RESULTS: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95% CI 0.664-0.804) and 0.658 (95% CI 0.587-0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95% CI 0.578-0.766) and 0.666 (95% CI 0.574-0.758) for OS and PFS, respectively. Kaplan-Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score. CONCLUSIONS: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

External validation of nodal failure prediction models including radiomics in head and neck cancer.

PURPOSE: To externally validate the previously published pre-treatment prediction models for lymph nodes failure after definitive radiotherapy in head and neck squamous cell carcinoma (HNSCC) patients. MATERIALS AND METHODS: This external validation cohort consisted of 143 node positive HNSCC patients treated between July 2007 and June 2016 by curative radiotherapy with or without either cisplatin or cetuximab. Imaging and pathology reports during follow-up were analyzed to indicate persisting or recurring nodes. The previously established clinical, radiomic and combined models were validated on this cohort by assessing the concordance index (c-index) and model calibration. RESULTS: Overall 113 patients with 374 pLNs were suitable for final analysis. There were 20 (5.3%) nodal failures from 15 patients after a median follow-up of 36.1 months. Baseline characteristics and radiomic features were comparable to the training cohort. Both the radiomic model (Least-axis-length of lymph node (LALLN) and correlation of gray level co-occurrence matrix (Corre-GLCM)) and the combined model (T stage, gender, WHO performance score, LALLN and Corre-GLCM) showed good agreement between predicted and observed nodal control probabilities. The radiomic (c-index: 0.71; 95% confidence interval (CI): 0.59-0.84) and combined (c-index: 0.71; 95% CI: 0.59-0.82) models performed better than the clinical model (c-index: 0.57; 95% CI: 0.47-0.68) on this cohort, with a significant difference between the combined and clinical models (z-score test: p = 0.005). CONCLUSION: The combined model including clinical and radiomic features was externally validated and proved useful to predict nodal failures and could be helpful to guide treatment choices before and after curative radiation treatment for node positive HNSCC patients.

Intratreatment Tumor Volume Change During Definitive Chemoradiotherapy is Predictive for Treatment Outcome of Patients with Esophageal Carcinoma.

BACKGROUND: This study aimed to assess the predictive value of tumor volume changes of esophagus evaluated by serial computed tomography (CT) scans before, during, and after radical chemoradiotherapy (CRT) for treatment outcomes in patients with esophageal cancer (EC). METHODS: Fifty-three patients with histologically confirmed EC were included for analysis. Gross tumor volume of esophagus (GTVe) was manually contoured on the CT images before treatment, at a twentieth fraction of radiotherapy, at completion of CRT and three months after treatment. GTVe reduction ratio (RR) was calculated to reveal changes of tumor volume by time. The Kaplan-Meier method was used to estimate survival and for univariate analysis. The Cox regression model was performed for multivariate analysis. RESULTS: Predominant reduction of GTVe was observed during the first 20 fractions of radiotherapy. Age, pretreatment GTVe, GTVe three months after treatment and GTVe RR at twentieth fraction of radiotherapy were all significantly associated with overall survival (OS) in a univariate analysis. Gender was correlated with locoregional recurrence-free survival (LRRFS) in univariate analysis. Multivariate analysis showed that GTVe ≤20 cc, GTVe RR at twentieth fraction of radiotherapy ≥35% were positive predictive factors of OS and pretreatment GTVe ≤20 cc was prognostic for a favorable LRRFS. CONCLUSION: Pretreatment tumor volume and intratreatment volume reduction ratio are reliable prognostic factors for esophageal cancer treated with definitive CRT.

Can we safely reduce the radiation dose to the heart while compromising the dose to the lungs in oesophageal cancer patients?

PURPOSE: The aim of this study was to evaluate which clinical and treatment-related factors are associated with heart and lung toxicity in oesophageal cancer patients treated with chemoradiation (CRT). The secondary objective was to analyse whether these toxicities are associated with overall survival (OS). MATERIALS AND METHODS: The study population consisted of a retrospective cohort of 216 oesophageal cancer patients treated with curative CRT. Clinical and treatment related factors were analysed for OS and new pulmonary and cardiac events by multivariable regression analyses. The effect of these toxicities on OS was assessed by Kaplan Meyer analyses. RESULTS: Multivariable analysis revealed that pulmonary toxicity was best predicted by the mean lung dose. Cardiac complications were diverse; the most frequently occurring complication was pericardial effusion. Several cardiac dose parameters correlated with this endpoint. Patients developing radiation pneumonitis had significantly worse OS than patients without radiation pneumonitis, while no difference was observed in OS between patients with and without pericardial effusion. OS was best predicted by the V45 of the lung and tumour stage. None of the cardiac dose parameters predicted OS in multivariable analyses. CONCLUSION: Cardiac dose volume parameters predicted the risk of pericardial effusion and pulmonary dose volume parameters predicted the risk of radiation pneumonitis. However, in this patient cohort, pulmonary DVH parameters (V45) were more important for OS than cardiac DVH parameters. These results suggest that reducing the cardiac dose at the expense of the dose to the lungs might not always be a good strategy in oesophageal cancer patients.

Application of combined cerebrospinal fluid physicochemical parameters to detect intracranial infection in neurosurgery patients.

Routine test of cerebrospinal fluid (CSF), such as glucose concentrations, chloride ion, protein and leukocyte, as well as color, turbidity and clot, were important indicators for intracranial infection. However, there were no models to predict the intracranial infection with these parameters. We collected data of 221 cases with CSF positive-culture and 50 cases with CSF negative culture from January 1, 2016 to December 31, 2018 in the First Affiliated Hospital of Nanchang University, China. SPSS17.0 software was used to establish the model by adopting seven described indicators, and P < 0.05 was considered as statistically significant. Meanwhile, 40 cases with positive-culture and 10 cases with negative-culture were selected to verify the sensitivity and specificity of the model. The results showed that each parameter was significant in the model establishment (P < 0.05). To extract the above seven parameters, the interpretation model C was established, and C = 0.952-0.183 × glucose value (mmol/L) - 0.024 × chloride ion value (mmol/L)- 0.000122 × protein value (mg/L) - 0.0000859 × number of leukocytes per microliter (× 106/L) + 1.354 × color number code + 0.236 × turbidity number code + 0.691 × clot number code. In addition, the diagnostic sensitivity and specificity of the model were 85.0 and 100%, respectively. The combining application of seven physicochemical parameters of CSF might be of great value in the diagnosis of intracranial infection for adult patients.

Pre-treatment radiomic features predict individual lymph node failure for head and neck cancer patients.

BACKGROUND AND PURPOSE: To develop and validate a pre-treatment radiomics-based prediction model to identify pathological lymph nodes (pLNs) at risk of failures after definitive radiotherapy in head and neck squamous cell carcinoma patients. MATERIALS AND METHODS: Training and validation cohorts consisted of 165 patients with 558 pLNs and 112 patients with 467 pLNs, respectively. All patients were primarily treated with definitive radiotherapy, with or without systemic treatment. The endpoint was the cumulative incidence of nodal failure. For each pLN, 82 pre-treatment CT radiomic features and 7 clinical features were included in the Cox proportional-hazard analysis. RESULTS: There were 68 and 23 nodal failures in the training and validation cohorts, respectively. Multivariable analysis revealed three clinical features (T-stage, gender and WHO Performance-status) and two radiomic features (Least-axis-length representing nodal size and gray level co-occurrence matrix based - Correlation representing nodal heterogeneity) as independent prognostic factors. The model showed good discrimination with a c-index of 0.80 (0.69-0.91) in the validation cohort, significantly better than models based on clinical features (p < 0.001) or radiomics (p = 0.003) alone. High- and low-risk groups were defined by using thresholds of estimated nodal failure risks at 2-year of 60% and 10%, resulting in positive and negative predictive values of 94.4% and 98.7%, respectively. CONCLUSION: A pre-treatment prediction model was developed and validated, integrating the quantitative radiomic features of individual lymph nodes with generally used clinical features. Using this prediction model, lymph nodes with a high failure risk can be identified prior to treatment, which might be used to select patients for intensified treatment strategies targeted on individual lymph nodes.

The effect of text message reminders on medication adherence among patients with coronary heart disease: A systematic review and meta-analysis.

BACKGROUND: To determine the effectiveness of text message reminders (TMR) on medication adherence (MA) and to investigate the effects of TMR on clinical outcomes. METHODS: The PubMed, Cochrane library, EMbase, and China Biology Medicine databases were searched for randomized-controlled trials with TMR as the intervention for patients with coronary heart disease. Two reviewers independently extracted data and assessed the risk of bias. Meta-analysis was conducted using Stata 15.0 software. RESULTS: In total, 1678 patients in 6 trials were included. Compared with the control group, the MA was 2.85 times greater among the intervention group (RR [relative risk] 2.85; 95% confidence interval [CI] 1.07-7.58). TMR reduced systolic blood pressure (BP) (weighted mean difference) = -6.51; 95% CI -9.79 to -3.23), cholesterol (standard mean difference = -0.26; 95% CI -0.4 to -0.12) and increased the number of patients with BP <140/90 mm Hg (RR 1.39; 95% CI 1.26-1.54). CONCLUSION: TMR significantly promoted MA and reduced systolic BP, cholesterol level, and body mass index, but had no effect on mortality, diastolic BP, or lipoproteins. However, substantial heterogeneity existed in our analyses.

Delta-radiomics features during radiotherapy improve the prediction of late xerostomia.

The response of the major salivary glands, the parotid glands, to radiation dose is patient-specific. This study was designed to investigate whether parotid gland changes seen in weekly CT during treatment, quantified by delta-radiomics features (Δfeatures), could improve the prediction of moderate-to-severe xerostomia at 12 months after radiotherapy (Xer12m). Parotid gland Δfeatures were extracted from in total 68 planning and 340 weekly CTs, representing geometric, intensity and texture characteristics. Bootstrapped forward variable selection was performed to identify the best predictors of Xer12m. The predictive contribution of the resulting Δfeatures to a pre-treatment reference model, based on contralateral parotid gland mean dose and baseline xerostomia scores (Xerbaseline) only, was evaluated. Xer12m was reported by 26 (38%) of the 68 patients included. The most predictive Δfeature was the contralateral parotid gland surface change, which was significantly associated with Xer12m for all weeks (p < 0.04), but performed best for week 3 (ΔPG-surfacew3; p < 0.001). Moreover, ∆PG-surfacew3 showed a significant predictive contribution in addition to the pre-treatment reference model (likelihood-ratio test; p = 0.003), resulting in a significantly better model performance (AUCtrain = 0.92; AUCtest = 0.93) compared to that of the pre-treatment model (AUCtrain = 0.82; AUCtest = 0.82). These results suggest that mid-treatment parotid gland changes substantially improve the prediction of late radiation-induced xerostomia.

The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation.

OBJECTIVES: The aim of this study was to investigate whether quantitative CT image-biomarkers (IBMs) can improve the prediction models with only classical prognostic factors for local-control (LC), regional-control (RC), distant metastasis-free survival (DMFS) and disease-free survival (DFS) for head and neck cancer (HNC) patients. MATERIALS AND METHODS: The cohort included 240 and 204 HNC patients in the training and validation analysis, respectively. Clinical variables were scored prospectively and IBMs of the primary tumor and lymph nodes were extracted from planning CT-images. Clinical, IBM and combined models were created from multivariable Cox proportional-hazard analyses based on clinical features, IBMs, and both for LC, RC, DMFS and DFS. RESULTS: Clinical variables identified in the multivariable analysis included tumor-site, WHO performance-score, tumor-stage and age. Bounding-box-volume describing the tumor volume and irregular shape, IBM correlation representing radiological heterogeneity, and LN_major-axis-length showing the distance between lymph nodes were included in the IBM models. The performance of IBM LC, RC, DMFS and DFS models (c-index(validated):0.62, 0.80, 0.68 and 0.65) were comparable to that of the clinical models (0.62, 0.76, 0.70 and 0.66). The combined DFS model (0.70) including clinical features and IBMs performed significantly better than the clinical model. Patients stratified with the combined models revealed larger differences between risk groups in the validation cohort than with clinical models for LC, RC and DFS. For DMFS, the differences were similar to the clinical model. CONCLUSION: For prediction of HNC treatment outcomes, image-biomarkers performed as good as or slightly better than clinical variables.

Parotid gland fat related Magnetic Resonance image biomarkers improve prediction of late radiation-induced xerostomia.

PURPOSE: This study investigated whether Magnetic Resonance image biomarkers (MR-IBMs) were associated with xerostomia 12 months after radiotherapy (Xer12m) and to test the hypothesis that the ratio of fat-to-functional parotid tissue is related to Xer12m. Additionally, improvement of the reference Xer12m model based on parotid gland dose and baseline xerostomia, with MR-IBMs was explored. METHODS: Parotid gland MR-IBMs of 68 head and neck cancer patients were extracted from pre-treatment T1-weighted MR images, which were normalized to fat tissue, quantifying 21 intensity and 43 texture image characteristics. The performance of the resulting multivariable logistic regression models after bootstrapped forward selection was compared with that of the logistic regression reference model. Validity was tested in a small external cohort of 25 head and neck cancer patients. RESULTS: High intensity MR-IBM P90 (the 90th intensity percentile) values were significantly associated with a higher risk of Xer12m. High P90 values were related to high fat concentration in the parotid glands. The MR-IBM P90 significantly improved model performance in predicting Xer12m (likelihood-ratio-test; p = 0.002), with an increase in internally validated AUC from 0.78 (reference model) to 0.83 (P90). The MR-IBM P90 model also outperformed the reference model (AUC = 0.65) on the external validation cohort (AUC = 0.83). CONCLUSION: Pre-treatment MR-IBMs were associated to radiation-induced xerostomia, which supported the hypothesis that the amount of predisposed fat within the parotid glands is associated with Xer12m. In addition, xerostomia prediction was improved with MR-IBMs compared to the reference model.