CancerCalc

Clinical tools for oncology professionals

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Pulmonary nodule malignancy risk score

1. Select model:
Herder score Estimates probability that a lung nodule will be diagnosed as cancer.
Brock score Estimates probability that a lung nodule will be diagnosed as cancer over a 2 to 4 year period.

Age:
years


Nodule size:
mm


Current or former smoker:


Past history of extra-thoracic cancer:


Upper lobe tumour location:


Spiculation:


FDG-PET uptake:


Age:
years


Gender:


Family history of lung cancer:


Emphysema:


Nodule size:
mm


Nodule type:


Upper lobe location:


Nodule count:


Spiculation:


Score:
Interpretation:
Pulmonary nodules identified on radiological imaging pose a diagnostic challenge. Predictive models aid decision making in managing indeterminate pulmonary nodules.

Herder score:
This model is based on an initial retrospective cohort study by Swensen et al consisting of 629 patients (320 men, 309 women) from a US population with newly diagnosed solitary pulmonary nodules on chest radiography (1). Two-thirds of the cohort (n=419) were used to develop the prediction model and the remaining patients were used as a validation data-set.

The model was extended by Herder et al to include FDG PET scanning which increased the model accuracy (2). This study included 106 patients and the combined model using predictive variables and FDG-PET scanning achieved an AUC of 0.92 (95% CI 0.87-0.97).

The original model excluded patients with history of extra-thoracic malignancy diagnosed ≤ 5 years previously. Recent validation has shown similar accuracy in patients with more recent history of extra-thoracic cancer (3). Therefore, any history of extra-thoracic cancer should be answered ‘yes’.

The model has been further validated in independent studies (3,4). Al-ameri et al found that in patients undergoing FDG-PET imaging the Herder model provided greater accuracy than the Brock model.

Brock score:
This model was developed from a population of 2537 patients enrolled in the PanCan study and a validation dataset of 1090 patients enrolled in the BCCA study. The final model showed AUC of >0.90 in the test and validation cohorts (5).

Predictive models in clinical decision making:
A study by Gould et al suggested watchful waiting when post-test probability of malignancy was very low ( <2%), biopsy at lower post-test probabilities (2-20%) and surgery at higher post-test probabilities (>70%) (6).

References:
  1. Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med. 1997;157(8):849-855.
  2. Herder GJ, van Tinteren H, Golding RP, et al. Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. Chest. 2005;128(4):2490-2496. doi:10.1378/chest.128.4.2490
  3. Al-Ameri A, Malhotra P, Thygesen H, et al. Risk of malignancy in pulmonary nodules: A validation study of four prediction models. Lung Cancer. 2015;89(1):27-30. doi:10.1016/j.lungcan.2015.03.018
  4. Schultz EM, Sanders GD, Trotter PR, et al. Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules. Thorax. 2008;63(4):335-341. doi:10.1136/thx.2007.084731
  5. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of Cancer in Pulmonary Nodules Detected on First Screening CT. N Engl J Med. 2013;369(10):910-919. doi:10.1056/NEJMoa1214726
  6. Gould MK, Sanders GD, Barnett PG, et al. Cost-Effectiveness of Alternative Management Strategies for Patients with Solitary Pulmonary Nodules. Ann Intern Med. 2003;138(9):724-735. doi:10.7326/0003-4819-138-9-200305060-00009