Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation

Stability analysis remains a fundamental step in developing a successful imaging biomarker to personalize oncological strategies. This study proposes an in silico contour generation method for simulating segmentation variations to identify stable radiomic features. Ground-truth annotation provided for the whole prostate gland on the multi-parametric MRI sequences (T2w, ADC, and SUB-DCE) were perturbed to mimic segmentation differences observed among human annotators. In total, we generated 15 synthetic contours for a given image-segmentation pair. One thousand two hundred twenty-four unfiltered/filtered radiomic features were extracted applying Pyradiomics, followed by stability assessment using ICC(1,1). Stable features identified in the internal population were then compared with an external population to discover and report robust features. Finally, we also investigated the impact of a wide range of filtering strategies on the stability of features. The percentage of unfiltered (filtered) features that remained robust subjected to segmentation variations were T2w-36% (81%), ADC-36% (94%), and SUB-43% (93%). Our findings suggest that segmentation variations can significantly impact radiomic feature stability but can be mitigated by including pre-filtering strategies as part of the feature extraction pipeline.

Towards texture accurate slice interpolation of medical images using PixelMiner

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

Automated detection and segmentation of non-small cell lung cancer computed tomography images

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer

OBJECTIVES

To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction.

METHODS

In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3⁺, CD4⁺, and CD8⁺ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55).

RESULTS

iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001).

CONCLUSIONS

iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection.

KEY POINTS

• Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients

PURPOSE

The 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status.

METHOD

209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset. Radiomics features were extracted from T2- and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods. For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed.

RESULTS

Seven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75-0.86) and 0.76 (0.71-0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6-0.82) using cubic interpolation and 0.72 (0.6-0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation).

CONCLUSIONS

MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation.

Structural and functional radiomics for lung cancer

INTRODUCTION

Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.

METHODS

Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer.

CONCLUSION

The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."

Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures

BACKGROUND

Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature.

MATERIAL AND METHODS

A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [¹⁸F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features.

RESULTS

A 11 feature "disease-agnostic CT model" reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62-0.94), 0.82 (95% CI, 0.67-0.96) and 0.78 (95% CI, 0.67-0.89) in three external validation datasets. A "disease-agnostic FDG-PET model" reached an AUC of 0.73 (0.95% CI, 0.49-0.97) in validation by combining 5 features. The highest "lung-specific CT model" reached an AUC of 0.80 (0.95% CI, 0.65-0.95) in validation with 4 CT features, while the "H&N-specific CT model" reached an AUC of 0.84 (0.95% CI, 0.64-1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80).

CONCLUSION

The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials.

Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

Resistance to chemotherapy often results from dysfunctional apoptosis, however multiple proteins with overlapping functions regulate this pathway. We sought to determine whether an extensively validated, deterministic apoptosis systems model, 'DR_MOMP', could be used as a stratification tool for the apoptosis sensitiser and BCL-2 antagonist, ABT-199 in patient-derived xenograft (PDX) models of colorectal cancer (CRC). Through quantitative profiling of BCL-2 family proteins, we identified two PDX models which were predicted by DR_MOMP to be sufficiently sensitive to 5-fluorouracil (5-FU)-based chemotherapy (CRC0344), or less responsive to chemotherapy but sensitised by ABT-199 (CRC0076). Treatment with ABT-199 significantly improved responses of CRC0076 PDXs to 5-FU-based chemotherapy, but showed no sensitisation in CRC0344 PDXs, as predicted from systems modelling. ¹⁸F-Fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT) scans were performed to investigate possible early biomarkers of response. In CRC0076, a significant post-treatment decrease in mean standard uptake value was indeed evident only in the combination treatment group. Radiomic CT feature analysis of pre-treatment images in CRC0076 and CRC0344 PDXs identified features which could phenotypically discriminate between models, but were not predictive of treatment responses. Collectively our data indicate that systems modelling may identify metastatic (m)CRC patients benefitting from ABT-199, and that ¹⁸F-FDG-PET could independently support such predictions.

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study

Background Solid components of part-solid nodules (PSNs) at CT are reflective of invasive adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular region are lacking. Purpose To develop and to validate radiomic signatures diagnosing invasive lung adenocarcinoma in PSNs compared with the Brock, clinical-semantic features, and volumetric models. Materials and Methods This retrospective multicenter study (https://ClinicalTrials.gov, NCT03872362) included 291 patients (median age, 60 years; interquartile range, 55-65 years; 191 women) from January 2013 to October 2017 with 297 PSN lung adenocarcinomas split into training (n = 229) and test (n = 68) data sets. Radiomic features were extracted from the different regions (gross tumor volume [GTV], solid, ground-glass, and perinodular). Random-forest models were trained using clinical-semantic, volumetric, and radiomic features, and an online nodule calculator was used to compute the Brock model. Performances of models were evaluated using standard metrics such as area under the curve (AUC), accuracy, and calibration. The integrated discrimination improvement was applied to assess model performance changes after the addition of perinodular features. Results The radiomics model based on ground-glass and solid features yielded an AUC of 0.98 (95% confidence interval [CI]: 0.96, 1.00) on the test data set, which was significantly higher than the Brock (AUC, 0.83 [95% CI: 0.72, 0.94]; P = .007), clinical-semantic (AUC, 0.90 [95% CI: 0.83, 0.98]; P = .03), volumetric GTV (AUC, 0.87 [95% CI: 0.78, 0.96]; P = .008), and radiomics GTV (AUC, 0.88 [95% CI: 0.80, 0.96]; P = .01) models. It also achieved the best accuracy (93% [95% CI: 84%, 98%]). Both this model and the model with added perinodular features showed good calibration, whereas adding perinodular features did not improve the performance (integrated discrimination improvement, -0.02; P = .56). Conclusion Separating ground-glass and solid CT radiomic features of part-solid nodules was useful in diagnosing the invasiveness of lung adenocarcinoma, yielding a better predictive performance than the Brock, clinical-semantic, volumetric, and radiomics gross tumor volume models. Online supplemental material is available for this article. See also the editorial by Nishino in this issue. Published under a CC BY 4.0 license.

Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC)

OBJECTIVES

Radiological characteristics and radiomics signatures can aid in differentiation between small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). We investigated whether molecular subtypes of large cell neuroendocrine carcinoma (LCNEC), i.e. SCLC-like (with pRb loss) vs. NSCLC-like (with pRb expression), can be distinguished by imaging based on (1) imaging interpretation, (2) semantic features, and/or (3) a radiomics signature, designed to differentiate between SCLC and NSCLC.

MATERIALS AND METHODS

Pulmonary oncologists and chest radiologists assessed chest CT-scans of 44 LCNEC patients for 'small cell-like' or 'non-small cell-like' appearance. The radiologists also scored semantic features of 50 LCNEC scans. Finally, a radiomics signature was trained on a dataset containing 48 SCLC and 76 NSCLC scans and validated on an external set of 58 SCLC and 40 NSCLC scans. This signature was applied on scans of 28 SCLC-like and 8 NSCLC-like LCNEC patients.

RESULTS

Pulmonary oncologists and radiologists were unable to differentiate between molecular subtypes of LCNEC and no significant differences in semantic features were found. The area under the receiver operating characteristics curve of the radiomics signature in the validation set (SCLC vs. NSCLC) was 0.84 (95% confidence interval (CI) 0.77-0.92) and 0.58 (95% CI 0.29-0.86) in the LCNEC dataset (SCLC-like vs. NSCLC-like).

CONCLUSION

LCNEC appears to have radiological characteristics of both SCLC and NSCLC, irrespective of pRb loss, compatible with the SCLC-like subtype. Imaging interpretation, semantic features and our radiomics signature designed to differentiate between SCLC and NSCLC were unable to separate molecular LCNEC subtypes, which underscores that LCNEC is a unique disease.

Correction: Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy

[This corrects the article DOI: 10.1371/journal.pone.0232639.].

Decision Support Systems in Oncology

Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.

Hypoxia PET Imaging with [18F]-HX4-A Promising Next-Generation Tracer

Hypoxia—a common feature of the majority of solid tumors—is a negative prognostic factor, as it is associated with invasion, metastasis and therapy resistance. To date, a variety of methods are available for the assessment of tumor hypoxia, including the use of positron emission tomography (PET). A plethora of hypoxia PET tracers, each with its own strengths and limitations, has been developed and successfully validated, thereby providing useful prognostic or predictive information. The current review focusses on [18F]-HX4, a promising next-generation hypoxia PET tracer. After a brief history of its development, we discuss and compare its characteristics with other hypoxia PET tracers and provide an update on its progression into the clinic. Lastly, we address the potential applications of assessing tumor hypoxia using [18F]-HX4, with a focus on improving patient-tailored therapies.

Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy

Introduction

In this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastasis.

Methods

Two comprehensive data sources were used: the Dutch Cancer Society Database (Alp 7072, DESIGN) and “Big Data To Decide” (BD2Decide). The gross tumor volumes (GTV) were delineated on contrast-enhanced CT. Radiomic features were extracted using the RadiomiX Discovery Toolbox (OncoRadiomics, Liege, Belgium). Clinical patient features such as age, gender, performance status etc. were collected. Two machine learning methods were chosen for their ability to handle censored data: Cox proportional hazards regression and random survival forest (RSF). Multivariable clinical and radiomic Cox/ RSF models were generated based on significance in univariable cox regression/ RSF analyses on the held out data in the training dataset. Features were selected according to a decreasing hazard ratio for Cox and relative importance for RSF.

Results

A total of 444 patients with radiotherapy planning CT-scans were included in this study: 301 head and neck squamous cell carcinoma (HNSCC) patients in the training cohort (DESIGN) and 143 patients in the validation cohort (BD2DECIDE). We found that the highest performing model was a clinical model that was able to predict distant metastasis in oropharyngeal cancer cases with an external validation C-index of 0.74 and 0.65 with the RSF and Cox models respectively. Peritumoral radiomics based prediction models performed poorly in the external validation, with C-index values ranging from 0.32 to 0.61 utilizing both feature selection and model generation methods.

Conclusion

Our results suggest that radiomic features from the peritumoral regions are not useful for the prediction of time to OS, LR and DM.

Deep learning in fracture detection: a narrative review

Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machines to better represent and interpret complex data. Deep learning is a subset of AI represented by the combination of artificial neuron layers. In the last years, deep learning has gained great momentum. In the field of orthopaedics and traumatology, some studies have been done using deep learning to detect fractures in radiographs. Deep learning studies to detect and classify fractures on computed tomography (CT) scans are even more limited. In this narrative review, we provide a brief overview of deep learning technology: we (1) describe the ways in which deep learning until now has been applied to fracture detection on radiographs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10-7). In comparison of the full workflow (feature selection and classification), no significant difference in ROC was found between centralized and distributed models for both studied endpoints (DeLong p > 0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models.

Radiomics: from qualitative to quantitative imaging

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Objectives

Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM).

Methods

This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested.

Results

The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume.

Conclusions

Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients.

Key Points

• A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules.

• Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06597-8) contains supplementary material, which is available to authorized users.

Biological Determinants of Chemo-Radiotherapy Response in HPV-Negative Head and Neck Cancer: A Multicentric External Validation

Purpose: Tumor markers that are related to hypoxia, proliferation, DNA damage repair and stem cell-ness, have a prognostic value in advanced stage HNSCC patients when assessed individually. Here we aimed to evaluate and validate this in a multifactorial context and assess interrelation and the combined role of these biological factors in determining chemo-radiotherapy response in HPV-negative advanced HNSCC. Methods: RNA sequencing data of pre-treatment biopsy material from 197 HPV-negative advanced stage HNSCC patients treated with definitive chemoradiotherapy was analyzed. Biological parameter scores were assigned to patient samples using previously generated and described gene expression signatures. Locoregional control rates were used to assess the role of these biological parameters in radiation response and compared to distant metastasis data. Biological factors were ranked according to their clinical impact using bootstrapping methods and multivariate Cox regression analyses that included clinical variables. Multivariate Cox regression analyses comprising all biological variables were used to define their relative role among all factors when combined. Results: Only few biomarker scores correlate with each other, underscoring their independence. The different biological factors do not correlate or cluster, except for the two stem cell markers CD44 and SLC3A2 (r = 0.4, p < 0.001) and acute hypoxia prediction scores which correlated with T-cell infiltration score, CD8⁺ T cell abundance and proliferation scores (r = 0.52, 0.56, and 0.6, respectively with p < 0.001). Locoregional control association analyses revealed that chronic (Hazard Ratio (HR) = 3.9) and acute hypoxia (HR = 1.9), followed by stem cell-ness (CD44/SLC3A2; HR = 2.2/2.3), were the strongest and most robust determinants of radiation response. Furthermore, multivariable analysis, considering other biological and clinical factors, reveal a significant role for EGFR expression (HR = 2.9, p < 0.05) and T-cell infiltration (CD8⁺T-cells: HR = 2.2, p < 0.05; CD8⁺T-cells/Treg: HR = 2.6, p < 0.01) signatures in locoregional control of chemoradiotherapy-treated HNSCC. Conclusion: Tumor acute and chronic hypoxia, stem cell-ness, and CD8⁺ T-cell parameters are relevant and largely independent biological factors that together contribute to locoregional control. The combined analyses illustrate the additive value of multifactorial analyses and support a role for EGFR expression analysis and immune cell markers in addition to previously validated biomarkers. This external validation underscores the relevance of biological factors in determining chemoradiotherapy outcome in HNSCC.

Acute Hypoxia Profile is a Stronger Prognostic Factor than Chronic Hypoxia in Advanced Stage Head and Neck Cancer Patients

Hypoxic head and neck tumors respond poorly to radiotherapy and can be identified using gene expression profiles. However, it is unknown whether treatment outcome is driven by acute or chronic hypoxia. Gene expression data of 398 head and neck cancers was collected. Four clinical hypoxia profiles were compared to in vitro acute and chronic hypoxia profiles. Chronic and acute hypoxia profiles were tested for their association to outcome using Cox proportional hazard analyses. In an initial set of 224 patients, scores of the four clinical hypoxia profiles correlated with each other and with chronic hypoxia. However, the acute hypoxia profile showed a stronger association with local recurrence after chemoradiotherapy (p = 0.02; HR = 3.1) than the four clinical (chronic hypoxia) profiles (p = 0.2; HR = 0.9). An independent set of 174 patients confirmed that acute hypoxia is a stronger prognostic factor than chronic hypoxia for overall survival, progression-free survival, local and locoregional control. Multivariable analyses accounting for known prognostic factors substantiate this finding (p = 0.045; p = 0.042; p = 0.018 and p = 0.003, respectively). In conclusion, the four clinical hypoxia profiles are related to chronic hypoxia and not acute hypoxia. The acute hypoxia profile shows a stronger association with patient outcome and should be incorporated into existing prediction models.

Clinical risk factors of colorectal cancer in patients with serrated polyposis syndrome: a multicentre cohort analysis

OBJECTIVE

Serrated polyposis syndrome (SPS) is accompanied by an increased risk of colorectal cancer (CRC). Patients fulfilling the clinical criteria, as defined by the WHO, have a wide variation in CRC risk. We aimed to assess risk factors for CRC in a large cohort of patients with SPS and to evaluate the risk of CRC during surveillance.

DESIGN

In this retrospective cohort analysis, all patients with SPS from seven centres in the Netherlands and two in the UK were enrolled. WHO criteria were used to diagnose SPS. Patients who only fulfilled WHO criterion-2, with IBD and/or a known hereditary CRC syndrome were excluded.

RESULTS

In total, 434 patients with SPS were included for analysis; 127 (29.3%) were diagnosed with CRC. In a per-patient analysis ≥1 serrated polyp (SP) with dysplasia (OR 2.07; 95% CI 1.28 to 3.33), ≥1 advanced adenoma (OR 2.30; 95% CI 1.47 to 3.67) and the fulfilment of both WHO criteria 1 and 3 (OR 1.60; 95% CI 1.04 to 2.51) were associated with CRC, while a history of smoking was inversely associated with CRC (OR 0.36; 95% CI 0.23 to 0.56). Overall, 260 patients underwent surveillance after clearing of all relevant lesions, during which two patients were diagnosed with CRC, corresponding to 1.9 events/1000 person-years surveillance (95% CI 0.3 to 6.4).

CONCLUSION

The presence of SPs containing dysplasia, advanced adenomas and/or combined WHO criteria 1 and 3 phenotype is associated with CRC in patients with SPS. Patients with a history of smoking show a lower risk of CRC, possibly due to a different pathogenesis of disease. The risk of developing CRC during surveillance is lower than previously reported in literature, which may reflect a more mature multicentre cohort with less selection bias.

Is early Weight Bearing Allowed in Surgically Treated Talar Neck Fractures?

INTRODUCTION

Non-or partial weight bearing is frequently the standard treatment after peri-articular lower extremity fractures. Displaced talar neck fractures are severe injuries compromising vascularity of the corpus and consequently are at risk for non-union and avascular necrosis, the main reason to restrict weight bearing for up to three months according to most literature.

CASE PRESENTATION

We report a case of a 31-year old male with a high impact car accident. His pelvic ring and Hawkins II talar fracture were treated by open reduction and internal fixation. Rehabilitation was based on permissive weight bearing following wound healing. His fractures healed uneventfully and he was able to run freely, without any discomfort within 8 weeks. Radiological evaluation of the talus showed complete bone healing without signs of avascular necrosis. At one year follow-up, the patient is free of the symptoms.

CONCLUSION

We might consider changing the restricted or non-weight bearing protocol in surgically treated talar neck fractures at our centre and allow early weight bearing, based on body awareness and the creation of a safe environment during the rehabilitation phase.

Definition and taxonomy of interval colorectal cancers: a proposal for standardising nomenclature

OBJECTIVE

Interval colorectal cancers (interval CRCs), that is, cancers occurring after a negative screening test or examination, are an important indicator of the quality and effectiveness of CRC screening and surveillance. In order to compare incidence rates of interval CRCs across screening programmes, a standardised definition is required. Our goal was to develop an internationally applicable definition and taxonomy for reporting on interval CRCs.

DESIGN

Using a modified Delphi process to achieve consensus, the Expert Working Group on interval CRC of the Colorectal Cancer Screening Committee of the World Endoscopy Organization developed a nomenclature for defining and characterising interval CRCs.

RESULTS

We define an interval CRC as a "colorectal cancer diagnosed after a screening or surveillance exam in which no cancer is detected, and before the date of the next recommended exam". Guidelines and principles for describing and reporting on interval CRCs are provided, and clinical scenarios to demonstrate the practical application of the nomenclature are presented.

CONCLUSIONS

The Working Group on interval CRC of the World Endoscopy Organization endorses adoption of this standardised nomenclature. A standardised nomenclature will facilitate benchmarking and comparison of interval CRC rates across programmes and regions.

Nonpolypoid colorectal neoplasms: a challenge in endoscopic surveillance of patients with Lynch syndrome

BACKGROUND AND STUDY AIMS

Patients with Lynch syndrome may develop colorectal cancer (CRC), despite intensive colonoscopic surveillance. Nonpolypoid colorectal neoplasms might be a major contributor to the occurrence of these cancers. The aim of this case - control study was to compare the endoscopic appearance of colorectal neoplasms between patients with Lynch syndrome and control individuals at average risk for CRC.

PATIENTS AND METHODS

The endoscopists at the Maastricht University Medical Center were first given training to ensure familiarity with the appearance and classification of nonpolypoid lesions. Patients with Lynch syndrome and patients at average risk for CRC who underwent elective colonoscopy at the Center were prospectively included. Nonpolypoid lesions were defined as lesions with a height of less than half the diameter, and advanced histology was defined as the presence of high grade dysplasia or early cancer.

RESULTS

A total of 59 patients with Lynch syndrome (mean age 48.7 years, 47.5 % men) and 590 matched controls (mean age 50.2 years, 47.5 % men) were included. In patients with Lynch syndrome, adenomas were significantly more likely to be nonpolypoid than they were in controls: 43.3 % vs. 16.9 % (OR 3.60, 95 %CI 1.90 - 6.83; P < 0.001). This was particularly true for proximal adenomas: 58.1 % vs. 16.3 % (OR 6.93, 95 %CI 2.92 - 16.40; P < 0.001). Adenomas containing advanced histology were more often nonpolypoid in patients with Lynch syndrome than in controls (4/5, 80.0 % vs. 5/17, 29.4 %; P = 0.19). Serrated polyps were also more often nonpolypoid in patients with Lynch syndrome than in controls: 49.2 % vs. 20.4 % (OR 3.57, 95 %CI 1.91 - 6.68; P < 0.001).

CONCLUSIONS

In patients with Lynch syndrome, colorectal neoplasms are more likely to have a nonpolypoid shape than those from average risk patients, especially in the proximal colon. These findings suggest that proficiency in recognition and endoscopic resection of nonpolypoid colorectal lesions are needed to ensure colonoscopic prevention against CRC in this high risk population.

Endoscopic red flags for the detection of high-risk serrated polyps: an observational study

BACKGROUND AND STUDY AIMS

In routine practice, colonoscopy may fail to prevent colorectal cancer (CRC), especially in the proximal colon. A better endoscopic recognition of serrated polyps is important, as this pathway may explain some of the post-colonoscopy cancers. In this study, the endoscopic characteristics of serrated polyps were examined.

PATIENT AND METHODS

This was a cross-sectional, single-center study of all consecutive patients referred for elective colonoscopy during 1 year. The endoscopists were familiarized with the detection and treatment of nonpolypoid colorectal lesions. Serrated polyps were classified into high risk serrated polyps, defined as dysplastic or large (≥ 6 mm) proximal nondysplastic serrated polyps, and low risk serrated polyps including the remaining nondysplastic serrated polyps. Advanced colorectal neoplasms were defined as multiple (at least three),≥ 10 mm in size, high grade dysplastic adenomas or CRC.

RESULTS

A total of 2309 patients were included (46.1 % men, mean age 58.4 years), of whom 2.5 % (57) had at least one high risk serrated polyp and 13.9 % (322) had at least one advanced neoplasm. Overall, serrated polyps were more often nonpolypoid than adenomas (16.2 % vs. 11.1 %; P = 0.002). In total, 65 high risk serrated polyps were found, of which 43.1 % (28) displayed a nonpolypoid endoscopic appearance. Patients with advanced neoplasms were more likely to have synchronous high risk serrated polyps than patients without advanced neoplasms: OR 3.66 (95 % CI 2.03 - 6.61, P < 0.001).

CONCLUSIONS

High risk serrated polyps are frequently nonpolypoid and are associated with synchronous advanced colorectal neoplasms. Advanced colorectal neoplasms may therefore be considered red flags for the presence of high risk serrated polyps. Detection, diagnosis, and treatment of high risk serrated lesions may be important targets to improve the quality of colonoscopic cancer prevention.

Screening for colorectal cancer: medical and economic aspects

BACKGROUND

Colorectal cancer (CRC) is the second commonest cause of cancer death in the Western world. In The Netherlands, CRC causes about 4400 deaths per year, and its diagnosis and treatment make up for a large share of health-care costs.

METHODS

Review and discussioN.

RESULTS

Experts in the field presently assume that screening for CRC and its precursor lesions, colorectal adenomas (CRAs), could prevent death from colorectal neoplasia by more than 80%. Additionally, there is increasing acknowledgement that CRC screening programmes can save lives at a cost similar to, or even less than, the generally accepted breast cancer or cervical cancer screening programmes. Nonetheless, while neighbouring countries have taken vigorous measures to fight CRC, the Dutch are still hesitating in this matter. This is partly due to some yet unanswered questions concerning the acceptability of screening for CRC in the general population, the starting age and the frequency of screening, the type of screening tests to be used, and the programme organization. In this commentary, general epidemiological and pathogenetic aspects of CRC are addressed. In addition, some frequently asked questions (FAQ) and (very subjective) answers about screening for CRC are offered, as potential substrate for further in-depth discussions.

CONCLUSION

The emerging message for the community is that an effective national screening programme is urgently required to reduce the substantial morbidity and mortality from this disease.

Ratio between serum IL-8 and pepsinogen A/C: a marker for atrophic body gastritis

BACKGROUND AND AIMS

Elevated serum gastrin and a low pepsinogen A/C ratio are well-recognized markers for atrophic body gastritis (ABG). We have shown that the presence of body atrophy is also associated with elevated serum pro-inflammatory cytokines. This study tested the hypothesis that serum cytokines provide additional information to gastrin and pepsinogens in screening for ABG.

METHODS

Two hundred and twenty-six consecutive patients were investigated on referral for upper gastrointestinal endoscopy: 150 were patients with gastro-oesophageal reflux disease, receiving acid inhibitory medication either with proton pump inhibitors (n = 113) or with histamine2-receptor antagonists (n = 37), and 76 were nontreated controls, who had normal endoscopic findings. Gastric mucosal biopsies were sampled for histological examination (Sydney classification). Serum samples were analyzed for gastrin, chromogranin A (CgA), and pepsinogens A and C by RIA, and for the interleukins (IL)-1beta, IL-6, and IL-8 by ELISA.

RESULTS

Subjects with ABG had significantly higher serum gastrin (P < 0.01) and serum CgA (P < 0.01) levels and significantly lower pepsinogen A/C ratios (P < 0.001) than those without ABG. Additionally, serum IL-1beta, IL-6 and, especially, IL-8 levels were significantly higher in the subjects with than in those without ABG (P < 0.0001, for all cytokines). To optimize the detection of body atrophy we defined the ABG index: the ratio between the simultaneously measured IL-8 and pepsinogen A/C. The area under the ROC curve for the ABG index was significantly greater than that for serum gastrin and for serum pepsinogen A/C alone (0.91 +/- 0.029 vs. 0.72 +/- 0.042, and vs. 0.83 +/- 0.031, P = 0.018 and P = 0.049). Using the ABG index at a cut-off value of 1.8 pg mL-1, 91% of the cases were classified correctly.

CONCLUSIONS

The ratio between serum IL-8 and pepsinogen A/C accurately predicts the presence of ABG. We therefore propose the ABG index as a noninvasive screening test for ABG in population-based studies.

Changes in gastric mucosa and luminal environment during acid-suppressive therapy: a review in depth

Acid-suppressive therapy and subsequent changes in gastric mucosa and luminal environment rank highly amongst the investigated issues in gastroenterology over the past two to three decades. Herewith, we present an overview of these intragastric changes, particularly during long-term administration of acid-suppresive medication and concurrent infection with Helicobacter pylori. Current evidence indicates that: i) Long-term acid suppression facilitates the development of fundic ECL cell hyperplasia, especially in the presence of Helicobacter pylori. No neoplastic changes directly attributable to acid suppression have so far been demonstrated in humans. ii) Acid-suppressive therapy increases the risk of enteric infections. iii) Acid-suppressive therapy does not alter fat and mineral bioavailability, but may decrease the absorption of protein-bound vitamin B12. iv) Acid suppression invariably results in intragastric overgrowth of non-Helicobacter pylori bacterial species. The concurrent infection with Helicobacter pylori may promote this bacterial overgrowth and the intragastric formation of N-nitrosamines. v) Acid-suppressive therapy alters the natural course of Helicobacter pylori gastritis, transforming the antral-predominant pattern into a body-predominant pattern, which in turn may progress to body gland atrophy. The pathophysiology of this phenomenon is currently under investigation. vi) In view of the potential adverse effects of acid suppression in the presence of Helicobacter pylori, the screen-and-treat strategy is advocated for Helicobacter pylori in subjects considered for long-term treatment.

Serum chromogranin A as a screening test for gastric enterochromaffin-like cell hyperplasia during acid-suppressive therapy

BACKGROUND

Serum chromogranin A (CgA), a marker of neuroendocrine neoplasia, increases during profound gastric acid inhibition, possibly reflecting the trophic effect of gastrin on the enterochromaffin-like (ECL) cells.

AIMS

This study investigated the clinical value of serum CgA as a screening test for gastric fundic enterochromaffin-like (ECL) cell hyperplasia during acid-suppressive therapy.

METHOD

A consecutive series of 230 dyspeptic patients referred for upper gastrointestinal endoscopy was investigated in a cross-sectional design. They were 154 patients on continuous medium-term (6 weeks to one year) or long-term (longer than one year) acid inhibition with either proton pump inhibitors (PPIs, n = 117) or histamine2-receptor antagonists (H2RAs, n = 37) for gastro-oesophageal reflux disease, and 76 nontreated subjects, with normal endoscopic findings (control group). Fasting blood samples were analysed for gastrin and CgA. Gastric biopsy specimens (oxyntic mucosa) were examined for histological evaluation of gastritis (Sydney classification) and of ECL cell hyperplasia (Solcia classification).

RESULTS

Serum CgA levels correlated positively with serum gastrin, following a quadratic function (r = 0.78, P < 0.0001). Elevated serum CgA values during long-term acid inhibition correlated with the presence and severity of fundic ECL cell hyperplasia. Multivariate analysis identified hypergastrinaemia (P < 0.0001), duration of acid inhibition (P < 0.0001), H. pylori infection (P = 0.008), ECL cell hyperplasia (P = 0.012), and body gland atrophy (P = 0.043) as independent predictors of elevated serum CgA. In subjects on long-term acid inhibition (n = 123), serum CgA was equally sensitive but more specific than serum gastrin for the detection of ECL cell hyperplasia (sensitivity, 91.3% for both; specificity, 73% vs. 43%, P < 0.0001).

CONCLUSIONS

During long-term gastric acid inhibition, serum CgA levels reflect the presence and severity of fundic ECL cell hyperplasia. Serum CgA is therefore a useful screening test for gastric ECL cell proliferative changes within this context.

Double gastric infection with Helicobacter pylori and non-Helicobacter pylori bacteria during acid-suppressive therapy: increase of pro-inflammatory cytokines and development of atrophic gastritis

BACKGROUND

Long-term acid suppression may accelerate the development of atrophic gastritis in Helicobacter pylori-positive subjects. The pathogenetic mechanism remains unclear.

AIM

To test the hypothesis that gastric double infection with H. pylori and non-H. pylori bacterial species-during acid suppression-may result in an enhanced inflammatory response, contributing to the development of atrophic gastritis.

PATIENTS AND METHODS

A consecutive series of patients with gastro-oesophageal reflux disease undergoing treatment with proton pump inhibitors (n=113) or histamine2-receptor antagonists (H2-RAs) (n=37), and 76 non-treated dyspeptic controls were investigated. Gastric mucosal H. pylori and non-H. pylori bacteria, histological gastritis, H. pylori serology, and circulating interleukin (IL)-1beta, IL-6, and IL-8 were examined.

RESULTS

Patients on acid suppression with either proton pump inhibitors or H2-RAs had a similar prevalence of H. pylori infection to the controls, but a higher prevalence of non-H. pylori bacteria (61% and 60% vs. 29%, P < 0.0001 and P < 0.002). Both the presence of H. pylori and non-H. pylori bacteria were independent risk factors of atrophic gastritis (antrum: relative risks (RRs), 10.1 and 5.07; corpus: RRs, 11.74 and 6.38). A simultaneous presence of H. pylori and non-H. pylori bacteria was associated with a markedly increased risk of atrophic gastritis (antrum: RR, 20.25; corpus: RR, 20.38), compatible with a synergistic effect. Furthermore, the simultaneous presence of both types of bacteria was associated with higher cytokine levels than in patients without any type of bacteria. This increase was also greater than in patients with H. pylori infection alone (P < 0.001, for both IL-1beta and IL-8).

SUMMARY AND CONCLUSIONS

H. pylori-positive patients on long-term acid inhibition displayed three features: non-H. pylori bacterial growth; increased cytokine levels; and a higher risk of atrophic gastritis. We suggest that double infection with H. pylori and non-H. pylori bacteria is a major factor in the development of atrophic gastritis during gastric acid inhibition.

Non-Helicobacter pylori bacterial flora during acid-suppressive therapy: differential findings in gastric juice and gastric mucosa

BACKGROUND

Intragastric growth of non-Helicobacter pylori bacteria commonly occurs during acid-suppressive therapy. The long-term clinical consequences are still unclear.

AIM

To investigate the luminal and mucosal bacterial growth during gastric acid inhibition, in relation to the type and duration of acid-inhibitory treatment, as well as to concomitant H. pylori infection.

METHODS

A total of 145 patients on continuous acid inhibition with either proton pump inhibitors (n=109) or histamine2-receptor antagonists (H(2)RAs, n=36) for gastro-oesophageal reflux disease, and 75 dyspeptic patients without acid inhibition (control group) were included. At endoscopy, fasting gastric juice was obtained for pH measurement and bacteriological culture. Gastric biopsy specimens were examined for detection of H. pylori (immunohistochemistry) and of non-H. pylori bacteria (modified Giemsa stain-positive and immunohistochemistry-negative at the same location).

RESULTS

Non-H. pylori flora was detected in the gastric juice of 92 (41.8%) patients and in the gastric mucosa of 109 (49.6%) patients. In gastric juice, prevalence rate for non-H. pylori bacteria was higher in patients taking proton pump inhibitors than controls and those taking H(2)RAs (58.7% vs. 22.6% and vs. 30.6%, P < 0.0001 and P < 0.003, respectively), but did not differ statistically between H(2)RAs and controls. In gastric mucosa, prevalence rates for non-H. pylori bacteria were higher in patients taking proton pump inhibitors and H(2)RAs than in the controls (antrum: 46.9% and 48.6% vs. 25%, P < 0.05 for both; corpus: 52.2% and 56.8% vs. 23.7%, P < 0.001 for both), but did not differ between proton pump inhibitors and H(2)RAs. Both luminal and mucosal growth of non-H. pylori bacteria were significantly greater in H. pylori-positive than -negative patients taking proton pump inhibitors (P < 0.05 for both). Luminal growth of non-H. pylori flora increased with the intragastric pH level, whilst mucosal bacterial growth increased with the duration of acid inhibition.

CONCLUSIONS

Non-H. pylori flora not only contaminates the gastric juice but also colonizes the gastric mucosa of a large proportion of patients treated long-term with acid inhibition. The relationship between H. pylori and non-H. pylori bacteria in the pathogenesis of atrophic gastritis and gastric cancer needs further elucidation.

Serum gastrin and chromogranin A during medium- and long-term acid suppressive therapy: a case-control study

BACKGROUND

Serum chromogranin A (CgA) is regarded as a reliable marker of neuroendocrine proliferation. We previously described increased serum CgA levels during short-term profound gastric acid inhibition.

AIM

To investigate serum gastrin and CgA levels in dyspeptic patients during continuous medium- (6 weeks to 1 year), or long-term (1-8 years) gastric acid suppressive therapy.

PATIENTS AND METHODS

114 consecutive dyspeptic patients referred for upper gastrointestinal endoscopy were enrolled in a cross-sectional, case-control study [62 patients on continuous antisecretory therapy, either with proton pump inhibitors (n = 47) or H2-receptor antagonists (H2RA) (n = 15) for gastro-oesophageal reflux disease with or without Barrett's oesophagus or functional dyspepsia, and 52 age- and sex-matched patients without medical acid inhibition and with normal endoscopic findings (control group)]. Omeprazole doses ranged from 20 mg to 80 mg daily and ranitidine from 150 mg to 450 mg daily. Fasting serum CgA and serum gastrin levels were measured by radioimmunoassay (reference values: serum CgA < 4.0 nmol/L; serum gastrin < 85 ng/L).

RESULTS

Fasting serum CgA levels positively correlated with serum gastrin in the entire study population (r = 0. 55, P = 0.0001). Median serum CgA values were higher in patients treated with a proton pump inhibitor than H2RA [2.8 (2.0-5.9) nmol/L vs. 2 (1.9-2.3) nmol/L, P < 0.002] and controls [2.8 (2.0-5.9) nmol/L vs. 1.8 (1.5-2.2) nmol/L, P < 0.0001) and did not differ between patients treated with H2RA or controls. Serum gastrin and CgA levels in patients on proton pump inhibitor therapy positively correlated with the degree and duration of acid inhibition. Patients on long-term proton pump inhibitor therapy had significantly higher fasting serum gastrin and CgA than those on medium-term proton pump inhibitor therapy [127 (73-217) ng/L vs. 49 (29-78) ng/L, P < 0.0001 and 4.8 (2.8-8) ng/L vs. 2.1 (1.9-2.6) ng/L, P < 0.001]. No such relation was found in patients on medium- vs. long-term H2RA. Overall, patients with positive Helicobacter pylori serology had higher serum gastrin and CgA levels than those with negative H. pylori serology [51 (27-119) ng/L vs. 27 (14-79) ng/L, P = 0.01, 2.4 (1.9-3.4) nmol/L vs. 2.0 (1.7-2.5) nmol/L, P = 0.05].

CONCLUSIONS

During long-term continuous proton pump inhibitor treatment, serum gastrin and CgA levels are significantly elevated compared to H2RA treatment and nontreated dyspeptic controls. H. pylori infection seems to affect gastric ECL cell secretory function. Increased serum CgA values during long-term profound gastric acid inhibition could reflect either gastric enterochromaffin-like cell hyperfunction or proliferative changes.