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 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.

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.

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 (Xer_12m). 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 Xer_12m. The predictive contribution of the resulting Δfeatures to a pre-treatment reference model, based on contralateral parotid gland mean dose and baseline xerostomia scores (Xer_baseline) only, was evaluated. Xer_12m 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 Xer_12m for all weeks (p < 0.04), but performed best for week 3 (ΔPG-surface_w3; p < 0.001). Moreover, ∆PG-surface_w3 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 (AUC_train = 0.92; AUC_test = 0.93) compared to that of the pre-treatment model (AUC_train = 0.82; AUC_test = 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 (Xer_12m) and to test the hypothesis that the ratio of fat-to-functional parotid tissue is related to Xer_12m. Additionally, improvement of the reference Xer_12m 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 Xer_12m. High P90 values were related to high fat concentration in the parotid glands. The MR-IBM P90 significantly improved model performance in predicting Xer_12m (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 Xer_12m. In addition, xerostomia prediction was improved with MR-IBMs compared to the reference model.

Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters

PURPOSE

To develop and validate prediction models of overall survival (OS) for head and neck cancer (HNC) patients based on image biomarkers (IBMs) of the primary tumor and positive lymph nodes (Ln) in combination with clinical parameters.

MATERIAL AND METHODS

The study cohort was composed of 289 nasopharyngeal cancer (NPC) patients from China and 298 HNC patients from the Netherlands. Multivariable Cox-regression analysis was performed to select clinical parameters from the NPC and HNC datasets, and IBMs from the NPC dataset. Final prediction models were based on both IBMs and clinical parameters.

RESULTS

Multivariable Cox-regression analysis identified three independent IBMs (tumor Volume-density, Run Length Non-uniformity and Ln Major-axis-length). This IBM model showed a concordance(c)-index of 0.72 (95%CI: 0.65-0.79) for the NPC dataset, which performed reasonably with a c-index of 0.67 (95%CI: 0.62-0.72) in the external validation HNC dataset. When IBMs were added in clinical models, the c-index of the NPC and HNC datasets improved to 0.75 (95%CI: 0.68-0.82; p=0.019) and 0.75 (95%CI: 0.70-0.81; p<0.001), respectively.

CONCLUSION

The addition of IBMs from the primary tumor and Ln improved the prognostic performance of the models containing clinical factors only. These combined models may improve pre-treatment individualized prediction of OS for HNC patients.

Serum proteomic-based analysis identifying autoantibodies against PRDX2 and PRDX3 as potential diagnostic biomarkers in nasopharyngeal carcinoma

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a major head and neck cancer with high occurrence in Southeast Asia and southern China. We aimed to identify autoantibodies that may contribute to early detection of NPC.

METHODS

We used serological proteome analysis to identify candidate autoantibodies against tumor-associated antigens. Levels of autoantibodies and Epstein-Barr virus capsid antigen-IgA (VCA-IgA) were measured by ELISA in 129 patients with NPC and 100 normal controls. We employed receiver operating characteristics to calculate diagnostic accuracy.

RESULTS

Sera from patients with NPC yielded multiple spots, two of which were identified as PRDX2 and PRDX3. Levels of serum autoantibodies against PRDX2 and PRDX3 were significantly higher for patients with NPC than for normal controls (P < 0.01), respectively. Combined detection of autoantibodies against PRDX2 and PRDX3 and VCA-IgA provided a high diagnostic accuracy in NPC (an area under the curve (AUC) of 0.811 (95% CI 0.753-0.869), 66.7% sensitivity, and 95.0% specificity). This combination maintained diagnostic performance for early NPC with AUC value of 0.754 (95% CI 0.652-0.857), 50.0% sensitivity, and 95.0% specificity.

CONCLUSIONS

This study reports autoantibodies against PRDX2 and PRDX3 identified by a proteomic approach in sera from NPC patients. Our findings suggest that autoantibodies against PRDX2 and PRDX3 may serve as supplementary biomarkers to VCA-IgA for the screening and diagnosis of NPC.

A Dosimetric Study of Using Fixed-Jaw Volumetric Modulated Arc Therapy for the Treatment of Nasopharyngeal Carcinoma with Cervical Lymph Node Metastasis

PURPOSE

To study the dosimetric difference between fixed-jaw volumetric modulated radiotherapy (FJ-VMAT) and large-field volumetric modulated radiotherapy (LF-VMAT) for nasopharyngeal carcinoma (NPC) with cervical lymph node metastasis.

METHODS

Computed tomography (CT) datasets of 10 NPC patients undergoing chemoradiotherapy were used to generate LF-VMAT and FJ-VMAT plans in the Eclipse version 10.0 treatment planning system. These two kinds of plans were then compared with respect to planning-target-volume (PTV) coverage, conformity index (CI), homogeneity index (HI), organ-at-risk sparing, monitor units (MUs) and treatment time (TT).

RESULTS

The FJ-VMAT plans provided lower D2% of PGTVnd (PTV of lymph nodes), PTV1 (high-risk PTV) and PTV2 (low-risk PTV) than did the LF-VMAT plans, whereas no significant differences were observed in PGTVnx (PTV of primary nasopharyngeal tumor). The FJ-VMAT plans provided lower doses delivered to the planning organ at risk (OAR) volumes (PRVs) of both brainstem and spinal cord, both parotid glands and normal tissue than did the LF-VMAT plans, whereas no significant differences were observed with respect to the oral cavity and larynx. The MUs of the FJ-VMAT plans (683 ± 87) were increased by 22% ± 12% compared with the LF-VMAT plans (559 ± 62). In terms of the TT, no significant difference was found between the two kinds of plans.

CONCLUSIONS

FJ-VMAT was similar or slightly superior to LF-VMAT in terms of PTV coverage and was significantly superior in terms of OAR sparing, at the expense of increased MUs.

Autoantibody Signatures Combined with Epstein-Barr Virus Capsid Antigen-IgA as a Biomarker Panel for the Detection of Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is prevalent in Southern China and Southeast Asia, and autoantibody signatures may improve early detection of NPC. In this study, serum levels of autoantibodies against a panel of six tumor-associated antigens (p53, NY-ESO-1, MMP-7, Hsp70, Prx VI, and Bmi-1) and Epstein-Barr virus capsid antigen-IgA (VCA-IgA) were tested by enzyme-linked immunosorbent assay in a training set (220 NPC patients and 150 controls) and validated in a validation set (90 NPC patients and 68 controls). We used receiver-operating characteristics (ROC) to calculate diagnostic accuracy. ROC curves showed that use of these 6 autoantibody assays provided an area under curve (AUC) of 0.855 [95% confidence interval (CI), 0.818-0.892], 68.2% sensitivity, and 90.0% specificity in the training set and an AUC of 0.873 (95% CI, 0.821-0.925), 62.2% sensitivity, and 91.2% specificity in the validation set. Moreover, the autoantibody panel maintained diagnostic accuracy for VCA-IgA-negative NPC patients [0.854 (0.809-0.899), 67.8%, and 90.0% in the training set; 0.879 (0.815-0.942), 67.4%, and 91.2% in the validation set]. Importantly, combination of the autoantibody panel and VCA-IgA improved diagnostic accuracy for NPC versus controls compared with the autoantibody panel alone [0.911 (0.881-0.940), 81.4%, and 90.0% in the training set; 0.919 (0.878-0.959), 78.9%, and 91.2% in the validation set), as well as for early-stage NPC (0.944 (0.894-0.994), 87.9%, and 94.0% in the training set; 0.922 (0.808-1.000), 80.0%, and 92.6% in the validation set]. These results reveal autoantibody signatures in an optimized panel that could improve the identification of VCA-IgA-negative NPC patients, may aid screening and diagnosis of NPC, especially when combined with VCA-IgA.

Volumetric modulated arc therapy vs. c-IMRT for the treatment of upper thoracic esophageal cancer

OBJECTIVE

To compare plans using volumetric-modulated arc therapy (VMAT) with conventional sliding window intensity-modulated radiation therapy (c-IMRT) to treat upper thoracic esophageal cancer (EC).

METHODS

CT datasets of 11 patients with upper thoracic EC were identified. Four plans were generated for each patient: c-IMRT with 5 fields (5F) and VMAT with a single arc (1A), two arcs (2A), or three arcs (3A). The prescribed doses were 64 Gy/32 F for the primary tumor (PTV64). The dose-volume histogram data, the number of monitoring units (MUs) and the treatment time (TT) for the different plans were compared.

RESULTS

All of the plans generated similar dose distributions for PTVs and organs at risk (OARs), except that the 2A- and 3A-VMAT plans yielded a significantly higher conformity index (CI) than the c-IMRT plan. The CI of the PTV64 was improved by increasing the number of arcs in the VMAT plans. The maximum spinal cord dose and the planning risk volume of the spinal cord dose for the two techniques were similar. The 2A- and 3A-VMAT plans yielded lower mean lung doses and heart V50 values than the c-IMRT. The V20 and V30 for the lungs in all of the VMAT plans were lower than those in the c-IMRT plan, at the expense of increasing V5, V10 and V13. The VMAT plan resulted in significant reductions in MUs and TT.

CONCLUSION

The 2A-VMAT plan appeared to spare the lungs from moderate-dose irradiation most effectively of all plans, at the expense of increasing the low-dose irradiation volume, and also significantly reduced the number of required MUs and the TT. The CI of the PTVs and the OARs was improved by increasing the arc-number from 1 to 2; however, no significant improvement was observed using the 3A-VMAT, except for an increase in the TT.

More accurate definition of clinical target volume based on the measurement of microscopic extensions of the primary tumor toward the uterus body in international federation of gynecology and obstetrics Ib-IIa squamous cell carcinoma of the cervix

PURPOSE

To more accurately define clinical target volume for cervical cancer radiation treatment planning by evaluating tumor microscopic extension toward the uterus body (METU) in International Federation of Gynecology and Obstetrics stage Ib-IIa squamous cell carcinoma of the cervix (SCCC).

PATIENTS AND METHODS

In this multicenter study, surgical resection specimens from 318 cases of stage Ib-IIa SCCC that underwent radical hysterectomy were included. Patients who had undergone preoperative chemotherapy, radiation, or both were excluded from this study. Microscopic extension of primary tumor toward the uterus body was measured. The association between other pathologic factors and METU was analyzed.

RESULTS

Microscopic extension toward the uterus body was not common, with only 12.3% of patients (39 of 318) demonstrating METU. The mean (±SD) distance of METU was 0.32 ± 1.079 mm (range, 0-10 mm). Lymphovascular space invasion was associated with METU distance and occurrence rate. A margin of 5 mm added to gross tumor would adequately cover 99.4% and 99% of the METU in the whole group and in patients with lymphovascular space invasion, respectively.

CONCLUSION

According to our analysis of 318 SCCC specimens for METU, using a 5-mm gross tumor volume to clinical target volume margin in the direction of the uterus should be adequate for International Federation of Gynecology and Obstetrics stage Ib-IIa SCCC. Considering the discrepancy between imaging and pathologic methods in determining gross tumor volume extent, we recommend a safer 10-mm margin in the uterine direction as the standard for clinical practice when using MRI for contouring tumor volume.

Lymphangiogenesis and prognostic significance of vascular endothelial growth factor C in gastro-oesophageal junction adenocarcinoma

Vascular endothelial growth factor C (VEGF-C) is a crucial regulator of the development of lymphatic vessels and is involved in the lymph node metastasis of cancer. The levels of VEGF-C expression and lymphatic vessel density (LVD) in 128 gastro-oesophageal junction adenocarcinoma (GEJA) tissues were examined by immunohistochemistry and analysed for their association with clinicopathological features and disease-free survival. We found that 75.0% of tumour samples displayed strong immunoreactivity to VEGF-C. The levels of VEGF-C expression in the tumour tissues were associated with the stages of the clinical tumours and the lymph node metastasis status, but not with the age, gender and the size and type of tumours in the cohort. Similarly, LVD, as evaluated by anti-D2-40 staining, was also associated with the clinical stages of GEJA. The values of LVD were positively correlated with the levels of VEGF-C expression in these samples (r = 0.3760, P = 0.0001). High levels of VEGF-C expression and high values of LVD were associated with shorter periods of disease-free survival (DFS) in patients with GEJA (P < 0.001). In addition, GEJA at N1 and N2 stages, at T4 stage, chemotherapy after surgery, high levels of VEGF-C expression and lower marginal resection were independent factors for the prognosis of DFS in patients with GEJA. Our data indicate that VEGF-C may promote the lymphangiogenesis and lymphatic metastasis of GEJA and that VEGF-C may be a valuable biomarker for the diagnosis of lymphatic metastasis and a prognostic factor of the survival of patients with GEJA.