Active data enrichment by learning what to annotate in digital pathology

Our work aims to link pathology with radiology with the goal to improve the early detection of lung cancer. Rather than utilising a set of predefined radiomics features, we propose to learn a new set of features from histology. Generating a comprehensive lung histology report is the first vital step toward this goal. Deep learning has revolutionised the computational assessment of digital pathology images. Today, we have mature algorithms for assessing morphological features at the cellular and tissue levels. In addition, there are promising efforts that link morphological features with biologically relevant information. While promising, these efforts mostly focus on narrow, well-defined questions. Developing a comprehensive report that is required in our setting requires an annotation strategy that captures all clinically relevant patterns specified in the WHO guidelines. Here, we propose and compare approaches aimed to balance the dataset and mitigate the biases in learning by automatically prioritising regions with clinical patterns underrepresented in the dataset. Our study demonstrates the opportunities active data enrichment can provide and results in a new lung-cancer dataset annotated to a degree that is not readily available in the public domain

Impaired pulmonary ventilation beyond pneumonia in COVID-19: A preliminary observation

BACKGROUND: Coronavirus disease 2019 (COVID-19) may severely impair pulmonary function and cause hypoxia. However, the association of COVID-19 pneumonia on CT with impaired ventilation remains unexplained. This pilot study aims to demonstrate the relationship between the radiological findings on COVID-19 CT images and ventilation abnormalities simulated in a computational model linked to the patients\u27 symptoms. METHODS: Twenty-five patients with COVID-19 and four test-negative healthy controls who underwent a baseline non-enhanced CT scan: 7 dyspneic patients, 9 symptomatic patients without dyspnea, and 9 asymptomatic patients were included. A 2D U-Net-based CT segmentation software was used to quantify radiological futures of COVID-19 pneumonia. The CT image-based full-scale airway network (FAN) flow model was employed to assess regional lung ventilation. Functional and radiological features were compared across groups and correlated with the clinical symptoms. Heterogeneity in ventilation distribution and ventilation defects associated with the pneumonia and the patients\u27 symptoms were assessed. RESULTS: Median percentage ventilation defects were 0.2% for healthy controls, 0.7% for asymptomatic patients, 1.2% for symptomatic patients without dyspnea, and 11.3% for dyspneic patients. The median of percentage pneumonia was 13.2% for dyspneic patients and 0% for the other groups. Ventilation defects preferentially affected the posterior lung and worsened with increasing pneumonia linearly (y = 0.91x + 0.99, R2 = 0.73) except for one of the nine dyspneic patients who had disproportionally large ventilation defects (7.8% of the entire lung) despite mild pneumonia (1.2%). The symptomatic and dyspneic patients showed significantly right-skewed ventilation distributions (symptomatic without dyspnea: 0.86 +/- 0.61, dyspnea 0.91 +/- 0.79) compared to the patients without symptom (0.45 +/- 0.35). The ventilation defect analysis with the FAN model provided a comparable diagnostic accuracy to the percentage pneumonia in identifying dyspneic patients (area under the receiver operating characteristic curve, 0.94 versus 0.96). CONCLUSIONS: COVID-19 pneumonia segmentations from CT scans are accompanied by impaired pulmonary ventilation preferentially in dyspneic patients. Ventilation analysis with CT image-based computational modelling shows it is able to assess functional impairment in COVID-19 and potentially identify one of the aetiologies of hypoxia in patients with COVID-19 pneumonia

Accurate subtyping of lung cancers by modelling class dependencies

Identifying subtypes and histological patterns is crucial for lung cancer diagnosis and treatment. Nevertheless, datasets with complete subtyping annotations are scarce, and most existing work primarily focuses on categorising lung cancers into fundamental types, omitting the distinction of adenocarcinoma patterns. We present a computational approach for a more comprehensive lung cancer subtyping from histology by modelling the dependencies between cancer subtypes and histological patterns in a multi-label setting. Our approach utilises slide-level labels indicating cancer subtypes as well as the presence of cancerassociated patterns, thereby alleviating the need for labourintensive region-based annotations. A new dataset with cancer-associated pattern labels is constructed and combined with publicly available datasets. We evaluate our model’s ability to simultaneously differentiate cancer subtypes and cancer-associated patterns. The result demonstrates that our modules enable conventional weakly-supervised classification models on multi-label problems, achieving subset accuracy of 84% when differentiating lung cancer subtypes and cancer-associated histological patterns

Risk factors for community-acquired pneumonia among adults in Kenya: a case-control study.

BACKGROUND: Pneumonia is a leading cause of morbidity and mortality among adults worldwide; however, the risk factors for community-acquired pneumonia in Africa are not well characterized. METHODS: The authors recruited 281 cases of community-acquired pneumonia and 1202 hospital controls among patients aged ≥15 years who attended Kilifi District Hospital/Coast Provincial General Hospital in Kenya between 1994 and 6. Cases were admissions with an acute illness with ≥2 respiratory signs and evidence of consolidation on a chest radiograph. Controls were patients without signs of pneumonia, frequency matched by age, sex and hospital. Risk factors related to socio-demographic factors, drug use, clinical history, contact patterns and exposures to indoor air pollution were investigated by questionnaire, anthropometric measurements and laboratory assays. Associations were evaluated using a hierarchical logistic regression model. RESULTS: Pneumonia was associated with human immunodeficiency virus (HIV) infection (Odds Ratio [OR] 2.06, 95% CI 1.44-3.08), anemia (OR 1.91, 1.31-2.74), splenomegaly (OR 2.04, 95% CI 1.14-3.41), recent history of pneumonia (OR 4.65, 95% CI 1.66-12.5), history of pneumonia >2 years previously (OR 17.13, 95% CI 5.01-60.26), coryza in the 2 weeks preceding hospitalization (OR 2.09, 95% CI 1.44-3.03), current smoking (2.19, 95% CI 1.39-3.70), use of khat (OR 3.44, 95% CI 1.72-7.15), use of snuff (OR 2.67, 95% CI 1.35-5.49) and contact with several animal species. Presence of a Bacillus Calmette-Guerin (BCG) scar was associated with protection (OR 0.51, 95% CI 0.32-0.82). The risk factors varied significantly by sex. CONCLUSION: Pneumonia in Kenyan adults was associated with global risk factors, such as HIV and smoking, but also with specific local factors like drug use and contact with animals. Intervention strategies should account for sex-specific differences in risk factors

Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules

OBJECTIVES: Investigate the effect of a novel Bayesian penalised likelihood (BPL) reconstruction algorithm on analysis of pulmonary nodules examined with 18F-FDG PET/CT, and to determine its effect on small, sub-10-mm nodules. METHODS: 18F-FDG PET/CTs performed for nodule evaluation in 104 patients (121 nodules) were retrospectively reconstructed using the new algorithm, and compared to time-of-flight ordered subset expectation maximisation (OSEM) reconstruction. Nodule and background parameters were analysed semi-quantitatively and visually. RESULTS: BPL compared to OSEM resulted in statistically significant increases in nodule SUV(max) (mean 5.3 to 8.1, p < 0.00001), signal-to-background (mean 3.6 to 5.3, p < 0.00001) and signal-to-noise (mean 24 to 41, p < 0.00001). Mean percentage increase in SUV(max) (%ΔSUV(max)) was significantly higher in nodules ≤10 mm (n = 31, mean 73 %) compared to >10 mm (n = 90, mean 42 %) (p = 0.025). Increase in signal-to-noise was higher in nodules ≤10 mm (224 %, mean 12 to 27) compared to >10 mm (165 %, mean 28 to 46). When applying optimum SUV(max) thresholds for detecting malignancy, the sensitivity and accuracy increased using BPL, with the greatest improvements in nodules ≤10 mm. CONCLUSION: BPL results in a significant increase in signal-to-background and signal-to-noise compared to OSEM. When semi-quantitative analyses to diagnose malignancy are applied, higher SUV(max) thresholds may be warranted owing to the SUV(max) increase compared to OSEM. KEY POINTS: • Novel Bayesian penalised likelihood PET reconstruction was applied for lung nodule evaluation. • This was compared to current standard of care OSEM reconstruction. • The novel reconstruction generated significant increases in lung nodule signal-to-background and signal-to-noise. • These increases were highest in small, sub-10-mm pulmonary nodules. • Higher SUV(max)thresholds may be warranted when using semi-quantitative analyses to diagnose malignancy

18F-FDG PET/CT assessment of histopathologically confirmed mediastinal lymph nodes in non-small cell lung cancer using a penalised likelihood reconstruction

Purpose To investigate whether using a Bayesian penalised likelihood reconstruction (BPL) improves signal-to-background (SBR), signal-to-noise (SNR) and SUVmax when evaluating mediastinal nodal disease in non-small cell lung cancer (NSCLC) compared to ordered subset expectation maximum (OSEM) reconstruction. Materials and methods 18F-FDG PET/CT scans for NSCLC staging in 47 patients (112 nodal stations with histopathological confirmation) were reconstructed using BPL and compared to OSEM. Node and multiple background SUV parameters were analysed semi-quantitatively and visually. Results Comparing BPL to OSEM, there were significant increases in SUVmax (mean 3.2–4.0, p<0.0001), SBR (mean 2.2–2.6, p<0.0001) and SNR (mean 27.7–40.9, p<0.0001). Mean background SNR on OSEM was 10.4 (range 7.6–14.0), increasing to 12.4 (range 8.2–16.7, p<0.0001). Changes in background SUVs were minimal (largest mean difference 0.17 for liver SUVmean, p<0.001). There was no significant difference between either algorithm on receiver operating characteristic analysis (p=0.26), although on visual analysis, there was an increase in sensitivity and small decrease in specificity and accuracy on BPL. Conclusion BPL increases SBR, SNR and SUVmax of mediastinal nodes in NSCLC compared to OSEM, but did not improve the accuracy for determining nodal involvement

British Thoracic Society quality standards for the investigation and management of pulmonary nodules

IntroductionThe purpose of the quality standards document is to provide healthcare professionals, commissioners, service providers and patients with a guide to standards of care that should be met for the investigation and management of pulmonary nodules in the UK, together with measurable markers of good practice.MethodsDevelopment of British Thoracic Society (BTS) Quality Standards follows the BTS process of quality standard production based on the National Institute for Health and Care Excellence process manual for the development of quality standards.Results7 quality statements have been developed, each describing a key marker of high-quality, cost-effective care for the investigation and management of pulmonary nodules, and each statement is supported by quality measures that aim to improve the structure, process and outcomes of healthcare.DiscussionBTS Quality Standards for the investigation and management of pulmonary nodules form a key part of the range of supporting materials that the Society produces to assist in the dissemination and implementation of guideline recommendations.</jats:sec

Oxygen-enhanced MRI and radiotherapy in patients with oropharyngeal squamous cell carcinoma

Background and purpose: This study aimed to assess the role of T1 mapping and oxygen-enhanced MRI in patients undergoing radical dose radiotherapy for HPV positive oropharyngeal cancer, which has not yet been examined in an OE-MRI study. Materials and methods: Variable Flip Angle T1 maps were acquired on a 3T MRI scanner while patients (n = 12) breathed air and/or 100 % oxygen, before and after fraction 10 of the planned 30 fractions of chemoradiotherapy (‘visit 1’ and ‘visit 2’, respectively). The analysis aimed to assess to what extent (1) native R1 relates to patient outcome; (2) OE-MRI response relates to patient outcome; (3) changes in mean R1 before and after radiotherapy related to clinical outcome in patients with oropharyngeal squamous cell carcinoma. Results: Due to the radiotherapy being largely successful, the sample sizes of non-responder groups were small, and therefore it was not possible to properly assess the predictive nature of OE-MRI. The tumour R1 increased in some patients while decreasing in others, in a pattern that was overall consistent with the underlying OE-MRI theory and previously reported tumour OE-MRI responses. In addition, we discuss some practical challenges faced when integrating this technique into a clinical trial, with the aim that sharing this is helpful to researchers planning to use OE-MRI in future clinical studies. Conclusion: Altogether, these results suggest that further clinical OE-MRI studies to assess hypoxia and radiotherapy response are worth pursuing, and that there is important work to be done to improve the robustness of the OE-MRI technique in human applications in order for it to be useful as a widespread clinical technique

General practitioner referrals to one-stop clinics for symptoms that could be indicative of cancer: a systematic review of use and clinical outcomes.

BACKGROUND: One-stop clinics provide comprehensive diagnostic testing in one outpatient appointment. They could benefit patients with conditions, such as cancer, whose outcomes are improved by early diagnosis, and bring efficiency savings for health systems. OBJECTIVE: To assess the use and outcomes of one-stop clinics for symptoms where cancer is a possible diagnosis. DESIGN AND SETTING: Systematic review of studies reporting use and outcomes of one-stop clinics in primary care patients. METHOD: We searched MEDLINE, Embase, and Cochrane Library for studies assessing one-stop clinics for adults referred after presenting to primary care with any symptom that could be indicative of cancer. Study selection was carried out independently in duplicate with disagreements resolved through discussion. RESULTS: Twenty-nine studies were identified, most were conducted in the UK and observational in design. Few included a comparison arm. A pooled comparison of the cancer conversion rate of one-stop and multi-stop clinics was only possible for breast symptoms, and we found no significant difference. One-stop clinics were associated with significant reductions in the interval from referral to testing (15 versus 75 days) and more patients diagnosed on the same day (79% versus 25%) compared to multi-stop pathways. The majority of patients and GPs found one-stop clinics to be acceptable. CONCLUSION: This review found one-stop clinics were associated with reduced time from referral to testing, increased same day diagnoses, and were acceptable to patients and GPs. Our conclusions are limited by high levels of heterogeneity, scarcity of comparator groups, and the overwhelmingly observational nature of included studies