Radiologic Evaluation Of Skeletal Metastases: Role Of Plain Radiographs And Computed Tomography

Kalogeropoulou, Christina; Karachaliou, Anna; Zampakis, Peter

doi:10.1007/978-1-4020-9819-2_6

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Morphologically, various patterns can be seen such as irregularities of the tumor margin, irregularities of the internal structures reflecting necrotic tissues, and extraosseous extension of tumor lesion . CT scans usually reveal osteolysis or osteosclerosis in the malignant vertebral tumors . Hemangioma, a common benign vertebral tumor, demonstrates high‐signal intensities in T1‐ and T2‐weighted images, remarkable enhancement on MRI, and a typical “polka‐dot sign” on CT, indicating thickened trabeculae in the vertebral body .…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of benign notochordal cell tumor of the spine: is a biopsy necessary?

Tateda

Hashimoto

Aizawa

et al. 2017

Clinical Case Reports

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Section: Discussionmentioning

confidence: 99%

Diagnosis of benign notochordal cell tumor of the spine: is a biopsy necessary?

Tateda

Hashimoto

Aizawa

et al. 2017

Clinical Case Reports

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“…Imaging is an important part of the management of bone metastasis ( 11 – 13 ). Computed tomography (CT) has the advantages of good anatomical resolution, soft-tissue contrast, and detailed morphology ( 14 , 15 ). It also facilitates simultaneous evaluation of the primary and metastatic lesions ( 12 , 16 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based algorithm improves radiologists’ performance in lung cancer bone metastases detection on computed tomography

Huo

Fāng

et al. 2023

Front. Oncol.

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PurposeTo develop and assess a deep convolutional neural network (DCNN) model for the automatic detection of bone metastases from lung cancer on computed tomography (CT)MethodsIn this retrospective study, CT scans acquired from a single institution from June 2012 to May 2022 were included. In total, 126 patients were assigned to a training cohort (n = 76), a validation cohort (n = 12), and a testing cohort (n = 38). We trained and developed a DCNN model based on positive scans with bone metastases and negative scans without bone metastases to detect and segment the bone metastases of lung cancer on CT. We evaluated the clinical efficacy of the DCNN model in an observer study with five board-certified radiologists and three junior radiologists. The receiver operator characteristic curve was used to assess the sensitivity and false positives of the detection performance; the intersection-over-union and dice coefficient were used to evaluate the segmentation performance of predicted lung cancer bone metastases.ResultsThe DCNN model achieved a detection sensitivity of 0.894, with 5.24 average false positives per case, and a segmentation dice coefficient of 0.856 in the testing cohort. Through the radiologists-DCNN model collaboration, the detection accuracy of the three junior radiologists improved from 0.617 to 0.879 and the sensitivity from 0.680 to 0.902. Furthermore, the mean interpretation time per case of the junior radiologists was reduced by 228 s (p = 0.045).ConclusionsThe proposed DCNN model for automatic lung cancer bone metastases detection can improve diagnostic efficiency and reduce the diagnosis time and workload of junior radiologists.

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“…Early detection is potentially achievable because cancer patients are frequently examined with computed tomography (CT) to detect local recurrence and distant metastasis. Owing to recent advances in CT scanners, bone metastases can be identified as small and faint lesions 12 , 13 . However, these are disguised by huge amounts of detailed anatomical information that often impedes the ability of radiologists to detect lesions within a reasonable time.…”

Section: Introductionmentioning

confidence: 99%

Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study

Onoue

Yakami

Nishio

et al. 2021

Sci Rep

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To determine whether temporal subtraction (TS) CT obtained with non-rigid image registration improves detection of various bone metastases during serial clinical follow-up examinations by numerous radiologists. Six board-certified radiologists retrospectively scrutinized CT images for patients with history of malignancy sequentially. These radiologists selected 50 positive and 50 negative subjects with and without bone metastases, respectively. Furthermore, for each subject, they selected a pair of previous and current CT images satisfying predefined criteria by consensus. Previous images were non-rigidly transformed to match current images and subtracted from current images to automatically generate TS images. Subsequently, 18 radiologists independently interpreted the 100 CT image pairs to identify bone metastases, both without and with TS images, with each interpretation separated from the other by an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Compared with interpretation without TS images, interpretation with TS images was associated with a significantly higher mean figure of merit (0.710 vs. 0.658; JAFROC analysis, P = 0.0027). Mean sensitivity at lesion-based was significantly higher for interpretation with TS compared with that without TS (46.1% vs. 33.9%; P = 0.003). Mean false positive count per subject was also significantly higher for interpretation with TS than for that without TS (0.28 vs. 0.15; P < 0.001). At the subject-based, mean sensitivity was significantly higher for interpretation with TS images than that without TS images (73.2% vs. 65.4%; P = 0.003). There was no significant difference in mean specificity (0.93 vs. 0.95; P = 0.083). TS significantly improved overall performance in the detection of various bone metastases.

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Radiologic Evaluation Of Skeletal Metastases: Role Of Plain Radiographs And Computed Tomography

Cited by 8 publications

References 33 publications

Diagnosis of benign notochordal cell tumor of the spine: is a biopsy necessary?

Diagnosis of benign notochordal cell tumor of the spine: is a biopsy necessary?

Deep learning-based algorithm improves radiologists’ performance in lung cancer bone metastases detection on computed tomography

Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study

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