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Objective: The aim of this study was to evaluate the performance of the automated computed tomography (CT) postprocessing software unfolded rib images for improved detection of both benign and malignant rib lesions during routine diagnostic workup of oncological patients.

Materials And Methods: One thousand eight in-patients and out-patients (63.66 ± 14.25 years; range, 18.67-95.67 years; 405 females and 603 males), undergoing chest CT between July 2018 to January 2019 at our institution, were retrospectively evaluated. Patients underwent chest CT alone or as part of a whole-body CT staging/restaging. The CT protocol consisted of the following: 120 kV; 100 mAs; matrix, 512 × 512; collimation, 0.6 mm; reconstructed section thickness of 3 mm and 1 mm using a soft tissue spatial resolution kernel (I30f) and a sharp kernel (B70f). Both transversal image data sets were used for "conventional" diagnosis including coronal reformates with 3-mm slice thickness. One-millimeter slice thickness image data sets of all patients were additionally directed from the scanner to a computational server where they were automatically postprocessed to 3-dimensional unfolded ribs. The "unfolding" of the rib using the centerline as an axis allows a synchronous display and rotation of all ribs by mouse scrolling. These postprocessed image data sets were evaluated in a separate reading session (approximately 4 weeks later). The readers had no information about the underlying medical history or clinical presentation. They were asked to record the lesion number, site of involvement along the rib (proximal, body, distal), number of the involved ribs, and the character of the lesion in terms of lytic versus sclerotic versus mixed lytic/sclerotic. The standard of reference was F-FDG PET, Ga-DOMITATE PET/CT, bone scan, or imaging follow-up (>6 months).

Results: From a total of 1008 evaluated patients, 763 (73.02%) were hemato-oncologic patients. A total of 104 rib lesions were found by transversal CT image reading, whereas the unfolded rib image reading detected 305 lesions. Eighty-nine were classified malignant, and 202 were classified benign. Detection of malignant rib lesions proved significant both for less than 1 cm (P < 0.02) and more than 1 cm in diameter (P < 0.007). The sensitivity, specificity, positive predictive value, and negative predictive value for detection of malignant rib lesions were 97.7%, 98.5%, 96.6%, and 99% for unfolding ribs, and 76.4%, 100%, 92.7%, and 90.5% for conventional (transversal) image reading, respectively. Detection of sclerotic rib lesions and lesions greater than 1 cm in diameter were significantly better (P < 0.01) for the unfolding rib algorithm.

Conclusions: The "unfolded rib" reformates are significantly superior for rib lesion detection compared with conventional transversal CT scan reading and should therefore be used in all patients, particularly those with an oncologic background.