Communication About the Probability of Cancer in Indeterminate Pulmonary Nodules

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

Thyroid Cancer Risk Communication in Patients with Thyroid Nodules

The objective of this study is to evaluate thyroid cancer risk clinician-patient communication among patients receiving usual counseling and counseling enhanced by a conversation aid. A secondary analysis of clinical visit recordings and post-visit surveys obtained during a trial assessing the impact of a conversation aid for patients with thyroid nodules was conducted. We assessed how thyroid cancer risk was communicated, different risk communication strategies between groups, and predictors of accurate cancer risk perception. Fifty-nine patients were analyzed. Most were women (90%) and middle-aged (median 57 years). A verbal description of thyroid cancer risk was present most frequently (83%) and was more frequent in the conversation aid than the usual care group (100% vs. 63%, p < 0.001). A numerical description using percentages was present in 41% of visits and was more frequent in the conversation aid group (59% vs. 19%, p = 0.012). Natural frequencies (7%) and positive/negative framing (10%) were utilized less commonly. Uncertainty about risks was not discussed. No predictors of accurate risk perception were identified. Clinicians most commonly present a verbal description of thyroid cancer risk. Less commonly, natural frequencies, negative/positive framing, or uncertainty is discussed. Clinicians caring for patients with thyroid nodules should be aware of different strategies for communicating thyroid cancer risk.

The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers

BACKGROUND

Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect.

METHODS

Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets.

RESULTS

Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 &gt; 0.97 for each) with fitted parameters closely aligned with professional society guidelines.

CONCLUSIONS

The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice.

IMPACT

We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

Patient-Centered, Guideline-Concordant Discussion and Management of Pulmonary Nodules

Providing guideline-concordant management of pulmonary nodules can present challenges when a patient's anxiety about cancer or fear of invasive procedures colors judgment. The way in which providers discuss and make decisions about how to evaluate a pulmonary nodule can affect patient satisfaction, distress, and adherence to evaluation. This article discusses the complexity of tailoring patient-provider communication, decision-making, and implementation of guidelines for pulmonary nodule evaluation to the individual patient, emphasizing the importance of how information is conveyed and the value of listening to and addressing patients' concerns. We summarize the relevant guideline recommendations and literature, and provide two case scenarios to illustrate a patient-centered approach to discussing and managing pulmonary nodules from our perspectives as a pulmonologist and thoracic surgeon.

Effect of a Rule-in Biomarker Test on Pulmonary Nodule Management: A Survey of Pulmonologists and Thoracic Surgeons

INTRODUCTION

The field of biomarker development is evolving to assist in determining benign from malignant pulmonary nodules. Although a prospective clinical utility would best to show how a biomarker affects patient treatment and outcomes, we sought to begin to understand how the results might alter management by determining how physicians would use the results of a rule-in blood test to manage pulmonary nodules.

MATERIALS AND METHODS

Pulmonologists and thoracic surgeons in the American College of Chest Physicians clinician database were invited to participate in an online survey. The participant demographic data were collected. Four hypothetical clinical vignettes were presented. The participants accessed the pretest probability (probability of cancer [pCA]) for malignancy and chose the management strategies as the case progressed. The management strategies chosen before and after the result of a rule-in biomarker test were compared and assessed for guideline concordance.

RESULTS

Of the 455 eligible participants who had opened the survey, 416 (92%) completed it: 332 pulmonologists and 84 thoracic surgeons. Although 91% of the participants were very comfortable managing nodules, depending on the case, 30% to 62% incorrectly assessed the pCA, with 22% to 62% overestimating the risk and 8% to 51% underestimating the risk. After a rule-in blood test result, the clinician change in management moved in the right direction in some cases but, in others, the physicians used the results incorrectly. Pulmonologists and thoracic surgeons differed in the management strategies, with surgeons recommending surgery more often.

CONCLUSIONS

Although the use of biomarker testing for pulmonary nodule evaluation is promising, without proper physician education, the potential for harm exists. Clinical utility studies are needed to appropriately inform the effect of biomarker use.

Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?

OBJECTIVES

Post-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally.

MATERIALS AND METHODS

A common cohort of 317 individuals with computed tomography-detected, solid nodules (80 malignant, 237 benign) were used to evaluate the MPM performance. We created a web-based application for this study that allows others to easily calibrate thresholds and analyze the performance of MPMs on their local cohort. Thirty patients with repeated imaging were tested for improved performance longitudinally.

RESULTS

Using calibrated thresholds, Mayo Clinic and Brock University (BU) MPMs performed the best (AUC = 0.63, 0.61) compared to the Veteran's Affairs (0.51) and Peking University (0.55). Only BU had consensus with the original MPM threshold; the other calibrated thresholds improved MPM accuracy. No significant improvements in accuracy were found longitudinally between time points.

CONCLUSIONS

Calibration to a common cohort can select the best-performing MPM for your institution. Without calibration, BU has the most stable performance in solid nodules ≥ 8 mm but has only moderate potential to refine subjects into appropriate workup. Application of MPM is recommended only at initial evaluation as no increase in accuracy was achieved over time.

KEY POINTS

• Post-imaging lung cancer risk mathematical predication models (MPMs) perform poorly on local populations without calibration. • An application is provided to facilitate calibration to new study cohorts: the Mayo Clinic model, the U.S. Department of Veteran's Affairs model, the Brock University model, and the Peking University model. • No significant improvement in risk prediction occurred in nodules with repeated imaging sessions, indicating the potential value of risk prediction application is limited to the initial evaluation.