Probability Of Malignancy In Patients Research Articles

Evaluation of LMR, NLR and PLR as predictors of malignancy in indeterminate thyroid nodules.

SUMMARYObjectiveThyroid nodules with indeterminate cytology represent 20% of all thyroid nodules. Inflammation plays an important role in cancer. Lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are independent prognostic scores in numerous cancers, although no study has documented their role in cytology indeterminate nodules. The aim of this study is to evaluate the role of LMR, NLR and PLR values as predictors of malignancy in patients with cytology indeterminate nodules.MethodsThis retrospective study analysed data from 298 patients with indeterminate thyroid nodule. Anatomopathological and haematological data were analysed, dividing the population into two groups. LMR, NLR and PLR values were determined using ROC curve and data were analysed using independent samples t-test, test of proportions, Fisher’s exact test and univariate and multivariate logistic regression.ResultsWe found that a baseline LMR value ≥ 4.09 was indicative of benignity of indeterminate nodule. The probability of malignancy in patients with LMR < 4.09 was 26 times higher than patients with a LMR value ≥ 4.09.ConclusionsThis study showed that only LMR has shown a concrete probability to find a thyroid cancer in patients with indeterminate nodules. Further studies are necessary to implement tailored treatment.

Malignancy Prediction Capacity and Possible Prediction Model of Circulating Tumor Cells for Suspicious Pulmonary Lesions.

More and more undetermined lung lesions are being identified in routine lung cancer screening. The aim of this study was to try to establish a malignancy prediction model according to the tumor presentations. From January 2017 to December 2018, 50 consecutive patients who were identified with suspicious lung lesions were enrolled into this study. Medical records were reviewed and tumor macroscopic and microscopic presentations were collected for analysis. Circulating tumor cells (CTC) were found to differ between benign and malignant lesions (p = 0.03) and also constituted the highest area under the receiver operation curve other than tumor presentations (p = 0.001). Since tumor size showed the highest sensitivity and CTC revealed the best specificity, a malignancy prediction model was proposed. Akaike information criterion (A.I.C.) of the combined malignancy prediction model was 26.73, which was lower than for tumor size or CTCs alone. Logistic regression revealed that the combined malignancy prediction model showed marginal statistical trends (p = 0.0518). In addition, the 95% confidence interval of combined malignancy prediction model showed less wide range than tumor size ≥ 0.7 cm alone. The calculated probability of malignancy in patients with tumor size ≥ 0.7 cm and CTC > 3 was 97.9%. By contrast, the probability of malignancy in patients whose tumor size was < 0.7 cm, and CTC ≤ 3 was 22.5%. A combined malignancy prediction model involving tumor size followed by the CTC count may provide additional information to assist decision making. For patients who present with tumor size ≥ 0.7 cm and CTC counts > 3, aggressive management should be considered, since the calculated probability of malignancy was 97.9%.

P42.05 Investigating the Accuracy of Clinical Mathematical Models for Estimating the Probability of Malignancy in Patients With Pulmonary Nodules

Pulmonary Nodules (PNs) have become increasingly common with the increased use of computed tomography (CT). Over the years, multiple models have been developed for the evaluation of PNs. However, a consensus has not been made on the diagnostic value of these models. PubMed, Embase, Cochrane Library and Web of Science were searched for studies reporting mathematical models for PN evaluation until March 2020. Eligible models were summarized, and network meta-analysis was performed on externally validated models (PROSPEROdatabase CRD42020154731). Twenty-three original models were proposed in 42 included articles. Age and nodule size were most commonly used in the models, while PET-CT results were used when collected. The Mayo model was validated in 28 studies. The area under the curve values of 4 models (PKU, Brock, Mayo, VA) with sufficient external validation (at least 5 independent cohorts) were 0.830, 0.785, 0.743, and 0.750. Surgical intervention group (SI) models demonstrated better results in SI patients with a pooled sensitivity and specificity of 0.80 (95%CI 0.73-0.86) and 0.75 (95%CI 0.71-0.79) and 0.68 (95%CI 0.50-0.84) and 0.71 (95%CI 0.38-0.93) in screening and outpatient group (SO) patients. For SO models, the pooled specificity decreased from 0.80 (95%CI 0.67-0.90) to 0.68 (95%CI 0.59-0.76), while the sensitivity changed from 0.74 (95%CI 0.65-0.82) to 0.72 (95%CI 0.63-0.80) when the study population changed from SO to SI patients. Artificial intelligence-based models show promising preliminary results. The Mayo model is the most widely used and validated model, while the PKU model yields the best results with certain external validation.A suitable model should be used on appropriate cohorts, but their clinical application needs further investigation.

Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial

BackgroundEstimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs.MethodsData from the UK Lung Cancer...

Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules.

Pulmonary nodules are a common clinical problem. The goals of their evaluation are to expedite the diagnosis and treatment of patients with malignant nodules and to minimize testing in patients with benign nodules. The approach to lung nodule evaluation is directed by the probability that the nodule is malignant. Estimation of the probability of malignancy can be performed subjectively with intuition and clinical experience or by using validated probability models that combine patient clinical characteristics with nodule imaging features to estimate a probability of malignancy. The accuracy and the generalizability of probability models depend on the clinical profile and the prevalence of malignancy in the population in which they were derived. In this article, we review available validated models to estimate the probability of malignancy in patients with pulmonary nodules and outline how they were derived, their limitations, and how they compare with each other and physician judgment. We conclude with a brief discussion of advances in probability models.

Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules

Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules

A retrospective validation study of three models to estimate the probability of malignancy in patients with small pulmonary nodules from a tertiary oncology follow-up centre

To estimate the probability of malignancy in small pulmonary nodules (PNs) based on clinical and radiological characteristics in a non-screening population that includes patients with a prior history of malignancy using three validated models. Retrospective data on clinical and radiological characteristics was collected from the medical records of 702 patients (379 men, 323 women; range 19-94 years) with PNs ≤12mm in diameter at a single centre. The final diagnosis was compared to the probability of malignancy calculated by one of three models (Mayo, VA, and McWilliams). Model accuracy was assessed by receiver operating characteristics (ROC). The models were calibrated by comparing predicted and observed rates of malignancy. The area under the ROC curve (AUC) was highest for the McWilliams model (0.82; 95% confidence interval [CI]: 0.78-0.91) and lowest for the Mayo model (0.58; 95% CI: 0.55-0.59). The VA model had an AUC of (0.62; 95% CI: 0.47-0.64). Performance of the models was significantly lower than that in the published literature. The accuracy of the three models is lower in a non-screening population with a high prevalence of prior malignancy compared to the papers that describe their development. To the authors' knowledge, this is the largest study to validate predictive models for PNs in a non-screening clinically referred patient population, and has potential implications for the implementation of predictive models.

Evaluation of undiagnosed solitary lung nodules according to the probability of malignancy in the American College of Chest Physicians (ACCP) evidence-based clinical practice guidelines

BackgroundThis study retrospectively investigated the clinical significance of undiagnosed solitary lung nodules removed by surgical resection.Patients and methodsWe retrospectively collected data on the age, smoking, cancer history, nodule size, location and spiculation of 241 patients who had nodules measuring 7 mm to 30 mm and a final diagnosis established by histopathology. We compared the final diagnosis of each patient with the probability of malignancy (POM) which was proposed by the American College of Chest Physicians (ACCP) guidelines.ResultsOf the 241 patients, 203 patients were diagnosed to have a malignant lung tumor, while 38 patients were diagnosed with benign disease. There were significant differences in the patients with malignant and benign disease in terms of their age, smoking history, nodule size and spiculation. The mean value and the standard deviation of the POM in patients with malignant tumors were 51.7 + 26.1%, and that of patients with benign lesions was 34.6 + 26.7%. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.67. The best cut-off value provided from the ROC curve was 22.6. When the cut-off value was set at 22.6, the sensitivity was 83%, specificity 52%, positive predictive value 90%, negative predictive value 36% and accuracy 77%, respectively.ConclusionsThe clinical prediction model proposed in the ACCP guidelines showed unsatisfactory results in terms of the differential diagnosis between malignant disease and benign disease of solitary lung nodules in our study, because the specificity, negative predictive value and AUC were relatively low.

Clinical model to estimate the pretest probability of malignancy in patients with pulmonary focal Ground-glass Opacity.

Effective strategies for managing patients with pulmonary focal Ground-glass Opacity (fGGO) depend on the pretest probability of malignancy. Estimating a clinical probability of malignancy in patients with fGGOs can facilitate the selection and interpretation of subsequent diagnostic tests. METHODS : Data from patients with pulmonary fGGO lesions, who were diagnosed at Sun Yat-sen University Cancer Center, was retrospectively collected. Multiple logistic regression analysis was used to identify independent clinical predictors for malignancy and to develop a clinical predictive model to estimate the pretest probability of malignancy in patients with fGGOs. One hundred and sixty-five pulmonary fGGO nodules were detected in 128 patients. Independent predictors for malignant fGGOs included a history of other cancers (odds ratio [OR], 0.264; 95% confidence interval [CI], 0.072 to 0.970), pleural indentation (OR, 8.766; 95% CI, 3.033-25.390), vessel-convergence sign (OR, 23.626; 95% CI, 6.200 to 90.027) and air bronchogram (OR, 7.41; 95% CI, 2.037 to 26.961). Model accuracy was satisfactory (area under the curve of the receiver operating characteristic, 0.934; 95% CI, 0.894 to 0.975), and there was excellent agreement between the predicted probability and the observed frequency of malignant fGGOs. We have developed a predictive model, which could be used to generate pretest probabilities of malignant fGGOs, and the equation could be incorporated into a formal decision analysis.

Anaplastic Large Cell Lymphoma Mimicking Fibrosing Mediastinitis

Fibrosing mediastinitis is rare. One type of this disease is idiopathic fibrosing mediastinitis. It is necessary to rule out malignancy in order to accurately diagnose fibrosing mediastinitis. We herein report a case of anaplastic large cell lymphoma diagnosed three months after a preliminary diagnosis of fibrosing mediastinitis. Glucocorticoid therapy was not successful in controlling disease progression. Immediately after initiating chemotherapy for lymphoma, the patient's symptoms improved dramatically and the mediastinal lesion decreased in size. Although few similar cases have been reported, hidden malignancy may present as fibrosing mediastinitis. Therefore, physicians should consider the probability of malignancy in patients with fibrosing mediastinitis because treatments may vary accordingly.

An Exploratory Research of the Endobronchial Ultrasonography Model to Estimate the Probability of Malignancy in Patients With Peripheral Pulmonary Lesions

SESSION TYPE: Lung Cancer Posters I

Probability of Malignancy in Solitary Versus Multiple Pulmonary Nodules in Positive FDG PET Scan

PURPOSE: Determination of the performance of PET in predicting the probability of malignancy in patients with solitary versus multiple pulmonary nodules with/without hilar and mediastinal lymphadenopathy.

Two Commonly Used Prediction Models (Mayo and VA) To Estimate the Probability of Malignancy in Patients With Solitary Pulmonary Nodules Are Not Applicable in a Country With a High Prevalence of Tuberculosis

Two Commonly Used Prediction Models (Mayo and VA) To Estimate the Probability of Malignancy in Patients With Solitary Pulmonary Nodules Are Not Applicable in a Country With a High Prevalence of Tuberculosis

A Clinical Model To Estimate the Pretest Probability of Lung Cancer in Patients With Solitary Pulmonary Nodules

Background:Estimating the clinical probability of malignancy in patients with a solitary pulmonary nodule (SPN) can facilitate the selection and interpretation of subsequent diagnostic tests.Methods:We used multiple logistic regression analysis to identify independent clinical predictors of malignancy and to develop a parsimonious clinical prediction model to estimate the pretest probability of malignancy in a geographically diverse sample of 375 veterans with SPNs. We used data from Department of Veterans Affairs (VA) administrative databases and a recently completed VA Cooperative Study that evaluated the accuracy of positron emission tomography (PET) scans for the diagnosis of SPNs.Results:The mean (± SD) age of subjects in the sample was 65.9 ± 10.7 years. The prevalence of malignant SPNs was 54%. Most participants were either current smokers (n = 177) or former smokers (n = 177). Independent predictors of malignant SPNs included a positive smoking history (odds ratio [OR], 7.9; 95% confidence interval [CI], 2.6 to 23.6), older age (OR, 2.2 per 10-year increment; 95% CI, 1.7 to 2.8), larger nodule diameter (OR, 1.1 per 1-mm increment; 95% CI, 1.1 to 1.2), and time since quitting smoking (OR, 0.6 per 10-year increment; 95% CI, 0.5 to 0.7). Model accuracy was very good (area under the curve of the receiver operating characteristic, 0.79; 95% CI, 0.74 to 0.84), and there was excellent agreement between the predicted probability and the observed frequency of malignant SPNs.Conclusions:Our prediction rule can be used to estimate the pretest probability of malignancy in patients with SPNs, and thereby facilitate clinical decision making when selecting and interpreting the results of diagnostic tests such as PET imaging.

Image analysis methods for solitary pulmonary nodule characterization by computed tomography

Computer software was designed for classifying solitary pulmonary nodules (SPNs) into benign and malignant from their CT images, using image analysis methods. The system made use of three features, computed from the CT density matrix of the SPN, and a class-discriminating algorithm. System evaluation was performed on 51 histologically confirmed SPNs of indeterminate CT diagnosis. Overall classification accuracy in distinguishing benign and malignant SPNs was 90.2%, while 83.3% of the benign and 93.9% of the malignant SPNs were correctly classified. The proposed system may be of value to the radiologist in assessing the probability of malignancy in patients with a solitary pulmonary nodule.

Unexplained lymphadenopathy in family practice. An evaluation of the probability of malignant causes and the effectiveness of physicians' workup.

This study reported here was undertaken to determine the probability of malignancy in patients presenting with unexplained lymphadenopathy in primary care practice and to estimate the effectiveness of current referral patterns by family physicians in relation to malignant disease. Clinical characteristics that may be discriminatory for malignant causes were also investigated. A retrospective analysis was performed of 82 patients who underwent biopsy for unexplained lymphadenopathy from 1982 to 1984; data regarding the incidence of unexplained lymphadenopathy and the referral rate for this problem were obtained from registration projects. A total of 29 malignant lymphadenopathies were identified for a prior probability of 1.1 percent and a posterior (after referral) probability of 11 percent. The ability of the family physician to refer malignant cases within four weeks after initial consultation (sensitivity of referral) was 80 to 90 percent; 91 to 98 percent of benign cases were not referred (specificity of referral). An increased likelihood of malignancy was associated with age over 40 years (4 percent) and supraclavicular lymphadenopathy (50 percent). The incidence of malignancy in patients presenting with unexplained lymphadenopathy to the family physicians is very low (1 to 2 percent). Nevertheless, despite the paucity of validated discriminatory factors, the family physicians perform a reasonably effective selection process toward referral and biopsy.