As a subclass of interstitial lung diseases, nonspecific interstitial pneumonia (NSIP), defined by an association of inflammation and fibrotic lung lesions, leads to progressive loss of pulmonary function and premature mortality. Among fibrosis manifestations, traction bronchiectasis (TB) is one of the most critical, its presence being associated to poor prognosis. Accurate follow up is crucial to treatment decision and adaptation. In this paper we propose quantitative biomarkers for TB, associating spatial localization and severity measures. Bronchial tree segmentation and TB identification first exploited a semi-automatic approach for the definition of the ground truth, then used an automatic method involving a convolutional neural network. The CNN training/testing database included 73/18 patients respectively, with both baseline and follow up exams at one year; the detection performance was assessed in terms of precision, recall and F1-score. For both ground truth and CNN-segmented data, the following TB biomarkers were derived: TB ratio over total airway, airway volume, normalized tree length, and airway caliber (absolute and relative) deviation from the normal 2-power decrease law. A correlation study between the TB biomarkers and the initial and follow up pulmonary function tests was conducted. Among ground truth-based TB biomarkers, airway volume, normalized tree length, absolute caliber deviation and TB ratio over total airway showed significant negative correlation with both initial and follow-up pulmonary function parameters, manifesting as potential prognosis biomarkers. Among CNN-based TB biomarkers, airway volume, normalized tree length, relative and absolute caliber deviation showed significant negative correlation with follow-up pulmonary function parameters confirming their predictive potential.