scholarly journals A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1610
Author(s):  
Leonardo Rundo ◽  
Roberta Eufrasia Ledda ◽  
Christian di Noia ◽  
Evis Sala ◽  
Giancarlo Mauri ◽  
...  
Keyword(s):  
Early Detection ◽  
Predictive Value ◽  
Low Dose ◽  
Low Cost ◽  
Characteristic Curve ◽  
Model Performance ◽  
Pulmonary Nodules ◽  
Quantitative Information ◽  
Test Dataset ◽  
Screening Programs

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Application “Lung Nodule" Software With Lungrads On Early Detection And Follow Up The Pulmonary Nodules By Lung Low Dose CT Findings

2020 ◽  
Author(s):  
Ha Hoang Thi Ngoc
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Pulmonary Nodule ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct

Background: A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. Content: The main risk factors are those of current or former smokers, aged 55 to 74 years with a smoking history of at least 1 pack-day. Low dose CT: Screening individuals with high risk of lung cancer by low dose CT scans could reduce lung cancer mortality by 20 percent compared to chest X-ray. Radiation dose has to maximum reduced but respect the rule of ALARA (As Low as Resonably Archivable). ACR-LungRADS 2014: Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. “Lung nodule” was applied as a smart phone application in order to have a quickly evaluation, especially the malignancy and management face on a pulmonary nodule.

Current Point-of-Care testing in cancer and future perspectives: a systematic review

2020 ◽  
Vol 30 (Supplement_2) ◽  
Author(s):  
M Rodrigues ◽  
I Andrade ◽  
R Cruz
Keyword(s):  
Systematic Review ◽  
Early Detection ◽  
Point Of Care ◽  
Low Cost ◽  
Rapid Test ◽  
Point Of Care Testing ◽  
Screening Programs ◽  
Low Resource ◽  
Resource Limited ◽  
Prevalent Disease

Abstract Introduction Cancer is the most prevalent disease worldwide, causing a major impact on society. Early detection and monitorization of the tumour can provide a more effective treatment. Point-of-care (POC) testing allows the patient to have a handheld test that gives the results rapidly. No expertise or special knowledge is required which is vital namely when carried out in low-resource areas. Recent studies analysed established and emerging biomarkers and how to incorporate them into POC devices, but a systematic review reporting the existing POC platforms for cancer is still not available. Objectives This systematic review aims to report current and recent advances for point-of-care testing in cancer. Methodology A literature review was conducted through research in the databases “PubMed” and “B-On” for relevant reviews published in the last ten years, using the keywords “Point-of-care testing” AND “Cancer” AND “Rapid Test” AND “Cancer detection”. Results In 2015 there were eight commercially available POC tests for prostate, bladder, colorectal, cervical, HPV-causing head and neck cancer, liver, breast and lung cancer. After 2018 a small number of POC devices were tested in screening programs and multicentric studies, and more recently, promising novel POC prototypes for early detection of cancer, namely a 3D prototype micro device for multiple singleplex RNA expression analysis in liver cancer and a POC microscopy prototype for digital diagnostics of breast cancer lymph node metastases, with potential to be used in resource-limited settings. Conclusion The use of POC testing can deliver accurate, fast results, and in the case of cancer it is no exception, contributing to the progression of treatment and reduction in cancer-related deaths. In low-resource settings a POC test is fundamental and it should be simple and low-cost. But there are limitations in the tests which is a challenge for improvement and investigation in the future.

scholarly journals Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules

2021 ◽  
Vol 10 ◽  
Author(s):  
Jieke Liu ◽  
Hao Xu ◽  
Haomiao Qing ◽  
Yong Li ◽  
Xi Yang ◽  
...  
Keyword(s):  
Low Dose ◽  
Validation Cohort ◽  
Standard Dose ◽  
Characteristic Curve ◽  
Pulmonary Nodules ◽  
Reconstruction Method ◽  
Solitary Pulmonary Nodules ◽  
Benign Lesions ◽  
Difference Matrix ◽  
Benign Nodules

ObjectivesThis study aimed to develop radiomic models based on low-dose CT (LDCT) and standard-dose CT to distinguish adenocarcinomas from benign lesions in patients with solid solitary pulmonary nodules and compare the performance among these radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The reproducibility of radiomic features between LDCT and standard-dose CT were also evaluated.MethodsA total of 141 consecutive pathologically confirmed solid solitary pulmonary nodules were enrolled including 50 adenocarcinomas and 48 benign nodules in primary cohort and 22 adenocarcinomas and 21 benign nodules in validation cohort. LDCT and standard-dose CT scans were conducted using same acquisition parameters and reconstruction method except for radiation dose. All nodules were automatically segmented and 104 original radiomic features were extracted. The concordance correlation coefficient was used to quantify reproducibility of radiomic features between LDCT and standard-dose CT. Radiomic features were selected to build radiomic signature, and clinical characteristics and radiomic signature were combined to develop radiomic nomogram for LDCT and standard-dose CT, respectively. The performance of radiomic models and Lung-RADS was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity.ResultsShape and first order features, and neighboring gray tone difference matrix features were highly reproducible between LDCT and standard-dose CT. No significant differences of AUCs were found among radiomic signature and nomogram of LDCT and standard-dose CT in both primary and validation cohort (0.915 vs. 0.919 vs. 0.898 vs. 0.909 and 0.976 vs. 0.976 vs. 0.985 vs. 0.987, respectively). These radiomic models had higher specificity than Lung-RADS (all correct P < 0.05), while there were no significant differences of sensitivity between Lung-RADS and radiomic models.ConclusionsThe diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT. The LDCT-based radiomic model with higher specificity and lower false-positive rate than Lung-RADS might help reduce overdiagnosis and overtreatment of solid pulmonary nodules in lung cancer screening.

scholarly journals Prognostic Value of Preoperative Absolute Lymphocyte Count in Children With Tetralogy of Fallot

2021 ◽  
Author(s):  
Xie Wu ◽  
Qipeng Luo ◽  
Zhanhao Su ◽  
Yinan Li ◽  
Hongbai Wang ◽  
...  
Keyword(s):  
Tetralogy Of Fallot ◽  
Lymphocyte Count ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Low Cost ◽  
Characteristic Curve ◽  
Model Performance ◽  
Area Under The Curve ◽  
Absolute Lymphocyte Count ◽  
Reverse Causality

Background Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease. Absolute lymphocyte count (ALC) is a low‐cost and easy‐to‐obtain inflammatory indicator; however, its association with the prognosis of patients with TOF remains unknown. This study aimed to determine the prognostic value of preoperative ALC in children with TOF. Methods and Results This retrospective study included 707 patients aged <6 years who underwent corrective operations for TOF between January 2016 and December 2018 in Fuwai Hospital, China. The end points were mortality, extracorporeal membrane oxygenation placement, postoperative hospital stay >30 days, and severe postoperative complications; patients were grouped on the basis of prognosis: poor prognosis (n=76) and good prognosis (n=631). Univariable and multivariable logistic regression analyses were performed to identify the independent risk factors for poor prognosis, on which a risk scoring system was based. The receiver operating characteristic curve was used to assess model performance. Using another model without ALC, the effect of the addition of ALC was assessed. Results suggested that ALC was an independent factor with a cutoff point of 4.36×10 9 /L. The addition of ALC improved the area under the curve from 0.771 to 0.781 ( P <0.001). To avoid reverse causality and further control for confounding factors, the patients were further divided on the basis of ALC level, and a propensity score matching was performed; 117 paired patients were identified for further analysis. Low ALC levels had an odds ratio of 3.500 (95% CI, 1.413–8.672). Conclusions Low preoperative ALC represents an independent predictor of poor prognosis in children with TOF.

scholarly journals Stent placement for benign esophageal leaks, perforations, and fistulae: a clinical prediction rule for successful leakage control

Endoscopy ◽  
2017 ◽  
Vol 50 (02) ◽  
pp. 98-108 ◽  
Author(s):  
Emo van Halsema ◽  
Wouter Kappelle ◽  
Bas Weusten ◽  
Robert Lindeboom ◽  
Mark van Berge Henegouwen ◽  
...  
Keyword(s):  
Stent Placement ◽  
Predictive Value ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Model Performance ◽  
Prediction Rule ◽  
Clinical Decision ◽  
Validation Sample ◽  
Clinical Predictors ◽  
Predicted Probability

Abstract Background and study aims Sealing esophageal leaks by stent placement allows healing in 44 % – 94 % of patients. We aimed to develop a prediction rule to predict the chance of successful stent therapy. Patients and methods In this multicenter retrospective cohort study, patients with benign upper gastrointestinal leakage treated with stent placement were included. We used logistic regression analysis including four known clinical predictors of stent therapy outcome. The model performance to predict successful stent therapy was evaluated in an independent validation sample. Results We included etiology, location, C-reactive protein, and size of the leak as clinical predictors. The model was estimated from 145 patients (derivation sample), and 59 patients were included in the validation sample. Stent therapy was successful in 55.9 % and 67.8 % of cases, respectively. The predicted probability of successful stent therapy was significantly higher in success patients compared with failure patients in both the derivation (P < 0.001) and validation (P < 0.001) samples. The area under the receiver operating characteristic curve was 74.1 % in the derivation sample and 84.7 % in the validation sample. When the model predicted ≥ 70 % chance of success, the positive predictive value was 79 % in the derivation sample and 87 % in the validation sample. When the model predicted ≤ 50 % chance of success, the negative predictive value was 64 % and 86 %, respectively. Conclusions This prediction rule, consisting of four clinical predictors, could identify patients with esophageal leaks who were likely to benefit from or fail on stent therapy. The prediction rule can support clinical decision-making when the predicted probability of success is ≥ 70 % or ≤ 50 %.

T2 mapping in orbital masses: preliminary study on differential diagnostic ability of T2 relaxation time

2019 ◽  
Vol 61 (5) ◽  
pp. 668-674
Author(s):  
Yan Zhou ◽  
Xiao-Quan Xu ◽  
Hao Hu ◽  
Guo-Yi Su ◽  
Hu Liu ◽  
...  
Keyword(s):  
Relaxation Time ◽  
Predictive Value ◽  
T2 Mapping ◽  
Characteristic Curve ◽  
Preoperative Assessment ◽  
Diagnostic Value ◽  
Quantitative Information ◽  
T2 Relaxation Time ◽  
T2 Relaxation ◽  
Orbital Masses

Background T2 mapping has been proven to be useful in tumor characterization. As to orbital masses, its diagnostic value needs to be investigated. Purpose To evaluate the usefulness of T2 mapping in orbital masses and the ability of T2 relaxation time in differentiating malignant from benign orbital masses. Material and Methods Forty-seven patients with solid orbital masses (33 benign and 14 malignant) who underwent T2 mapping examination for preoperative assessment were enrolled in the current study. T2 mapping was acquired using 16 TE values (range 12–192 ms; delta TE 12 ms). Mean T2 relaxation time was calculated based on the whole mass region of interest and compared between the malignant and benign groups using the unpaired t-test. Receiver operating characteristic curve analysis was adopted to calculate its diagnostic value. Results Malignant orbital masses showed significantly lower T2 relaxation time than benign masses (76.4 ± 13.0 ms vs. 119.1 ± 20.4 ms; P < 0.001). If setting a T2 relaxation time of 89.5 ms as the threshold value, optimal differentiating performance could be achieved (area under the curve 0.936; sensitivity 100.0%; specificity 87.9%; accuracy 91.5%; positive predictive value 77.8%; negative predictive value 100%). Conclusion T2 mapping and its derived T2 relaxation time could provide quantitative information and serve as a supplementary imaging marker for differentiating malignant from benign orbital masses.

scholarly journals Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer

2018 ◽  
Vol 45 (4) ◽  
pp. 1537-1549 ◽  
Author(s):  
Wookjin Choi ◽  
Jung Hun Oh ◽  
Sadegh Riyahi ◽  
Chia-Ju Liu ◽  
Feng Jiang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Low Dose ◽  
Pulmonary Nodules ◽  
Low Dose Ct

scholarly journals Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2714
Author(s):  
Piotr Widłak ◽  
Karol Jelonek ◽  
Agata Kurczyk ◽  
Joanna Żyła ◽  
Magdalena Sitkiewicz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Early Detection ◽  
Cancer Patients ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Lung Cancer Patients ◽  
Screening Programs ◽  
N 93 ◽  
Promising Source

Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.

The Role of a Decision Rule in Symptomatic Pulmonary Embolism Patients with a Non-high Probability Ventilation-perfusion Scan

1997 ◽  
Vol 78 (02) ◽  
pp. 794-798 ◽  
Author(s):  
Bowine C Michel ◽  
Philomeen M M Kuijer ◽  
Joseph McDonnell ◽  
Edwin J R van Beek ◽  
Frans F H Rutten ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Decision Rule ◽  
Predictive Value ◽  
Patient Population ◽  
High Probability ◽  
Characteristic Curve ◽  
Stepwise Logistic Regression ◽  
Pulmonary Angiogram ◽  
Symptomatic Pulmonary Embolism ◽  
Perfusion Scan

Summary Background: In order to improve the use of information contained in the medical history and physical examination in patients with suspected pulmonary embolism and a non-high probability ventilation-perfusion scan, we assessed whether a simple, quantitative decision rule could be derived for the diagnosis or exclusion of pulmonary embolism. Methods: In 140 consecutive symptomatic patients with a non- high probability ventilation-perfusion scan and an interpretable pulmonary angiogram, various clinical and lung scan items were collected prospectively and analyzed by multivariate stepwise logistic regression analysis to identify the most informative combination of items. Results: The prevalence of proven pulmonary embolism in the patient population was 27.1%. A decision rule containing the presence of wheezing, previous deep venous thrombosis, recently developed or worsened cough, body temperature above 37° C and multiple defects on the perfusion scan was constructed. For the rule the area under the Receiver Operating Characteristic curve was larger than that of the prior probability of pulmonary embolism as assessed by the physician at presentation (0.76 versus 0.59; p = 0.0097). At the cut-off point with the maximal positive predictive value 2% of the patients scored positive, at the cut-off point with the maximal negative predictive value pulmonary embolism could be excluded in 16% of the patients. Conclusions: We derived a simple decision rule containing 5 easily interpretable variables for the patient population specified. The optimal use of the rule appears to be in the exclusion of pulmonary embolism. Prospective validation of this rule is indicated to confirm its clinical utility.

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