The aim of this study was to optimize computed tomography (CT) surveillance of skeletal metastases in patients with breast cancer through the use of osseous subtraction maps between baseline and follow-up examinations created by a novel software algorithm. The new postprocessing algorithm segments the original bone followed by image intensity-based rigid alignment creating gray-shaded maps that highlight focal or diffuse loss or increase in bone attenuation.
Institutional review board was obtained for this retrospective data evaluation. A total of 33 consecutive patients (31 female; 2 male; mean age, 59.13 ± 12.68 years; range, 32–81 years) with breast cancer were included, who underwent 143 standardized baseline and follow-up CT examinations between February 2014 and June 2016. We classified bone metastases into lytic, sclerotic, and mixed osseous lesions. Any new osteolysis inside a known sclerotic lesion and enlargement of pre-existing sclerotic lesions were considered to represent progressive disease (PD), whereas no change was classified as stable disease (SD). Results were compared additionally with the course of the disease considering the entire skeleton and other involved organs. Software-created automated bone subtraction maps were compared with conventional CT interpretations of axial 5-mm and coronal 1-mm reformatted images. Region of interest measurements were used to quantify new lesions. Results were validated by clinical and CT follow-up. Reading time was evaluated.
Skeletal metastases were present in 17/33 (51%) patients (9 sclerotic, 2 lytic, 6 mixed) at baseline. The use of bone subtraction maps resulted in an overall change of response classification into PD in 9/33 (8.1%) patients. Compared with conventional CT evaluation, the bone subtraction maps disclosed 123 new or enlarging sclerotic and 32 new lytic metastases in 23/33 (30.9%) examinations. Mean attenuation of new bone lesions (sclerotic or lytic) significantly increased or decreased (P < 0.01) in all patients. Bone attenuation in pelvic areas without evident metastatic disease significantly increased in patients with PD (P = 0.019), whereas there was no change in SD (P = 0.076). Lesion-based sensitivity, specificity, accuracy, positive predictive values, and negative predictive values were 98.7%, 79.5%, 94.5%, 95.1%, and 94.5%, respectively. Interobserver agreement was good (κ = 0.80; P = 0.077). Reading time was significantly faster for the bone subtraction maps versus 5-mm axial images (P < 0.001).
Longitudinal bone subtraction maps increase the accuracy and efficiency of CT diagnosis of skeletal metastases in patients with breast cancer.
From the *Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Tübingen; †Siemens AG, Healthcare Sector Imaging and Therapy Division, Forchheim, Germany; and ‡Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
Received for publication October 10, 2016; and accepted for publication, after revision, November 7, 2016.
Disclosure: This study is owned by of the authors, who are in full control of the data. The developers and supplier of the new postprocessing tool had no influence on the realization of this project or on data interpretation. Jan Fritz received institutional research funds and speaker's honorarium from Siemens Healthcare USA and is a scientific advisor of Siemens Healthcare USA and Alexion Pharmaceuticals, Inc. One of the authors (H.D.) is Siemens AG staff member.
Correspondence to: Christopher Kloth, MD, Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany. E-mail: firstname.lastname@example.org.