A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies

Vassallo, Lorenzo; Traverso, Alberto; Agnello, M.; Bracco, Christian; Campanella, Delia; Chiara, Gabriele; Fantacci, Maria Evelina; Torres, E. López; Manca, Antonio; Saletta, M.; Giannini, Valentina; Mazzetti, Simone; Stasi, M.; Cerello, Piergiorgio; Regge, Daniele

doi:10.1007/s00330-018-5528-6

Cited by 26 publications

(13 citation statements)

References 38 publications

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“…The specificity of the CAD system with more than 13 false-positives per CT scan was only moderate compared to more recent systems with less than 5 false-positives and the tendency to higher sensitivities [22][23][24][25][26]. However, it proved to be useful since it significantly added sensitivity to the performance of the radiologist alone.…”

Section: Discussionmentioning

confidence: 88%

Contribution of CAD to the Sensitivity for Detecting Lung Metastases on Thin-Section CT – A Prospective Study with Surgical and Histopathological Correlation

Meybaum¹,

Graff²,

Fallenberg

et al. 2019

Fortschr Röntgenstr

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Purpose To assess the sensitivity of radiologists and a CAD system for the detection of lung metastases on thin-section computed tomographic (CT) scans prior to pulmonary metastasectomy. Materials and Methods All patients scheduled for resection of lung metastases were eligible for this prospective single-center trial. 95 patients with 115 surgical procedures (pulmonary metastasectomy using thoracotomy) were included. An experienced radiologist examined the CT scans for pulmonary metastases and documented his findings. A commercial CAD system was used as a second reader; additional CAD findings were recorded. A comparison of the sensitivity of the radiologist alone and with CAD was performed. Intraoperatively surgeons tried to identify the documented lesions and resected them as well as additionally palpable lesions. The standard of reference consisted of surgery and histopathology. Follow-up information for radiologically detected lesions missed during surgery was sought. Results 693 lesions (262 metastases) were detected radiologically or surgically, 646 of them were resected. The sensitivity of radiologists without CAD was 67.5 % for all lesions (87.4 % for metastases). CAD highly significantly (p < 0.001) increased the sensitivity to 77.9 % (92.7 %). During surgery, 143 additional lesions (19 metastases) were detected. 49 radiologically detected lesions were not palpable during surgery: 4 metastases, 5 benign lesions, and 40 lesions of an unknown nature. Conclusion CAD provides significant additional sensitivity for detecting lung metastases using MDCT compared to the performance of a radiologist alone. CT reveals a relevant number of non-palpable pulmonary lesions. Key Points: Citation Format

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Section: Discussionmentioning

confidence: 88%

Contribution of CAD to the Sensitivity for Detecting Lung Metastases on Thin-Section CT – A Prospective Study with Surgical and Histopathological Correlation

Meybaum¹,

Graff²,

Fallenberg

et al. 2019

Fortschr Röntgenstr

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“…There are two systems similar to CIRCUS CS. M5L ondemand Lung-CAD [37,38] is a web-and cloud-based CAD system dedicated to the automatic detection of pulmonary nodules. The detection algorithm is a combination of two independent algorithms: the Channeler Ant Model (lungCAM) and the voxel-based neural approach (VBNA).…”

Section: Discussionmentioning

confidence: 99%

Novel platform for development, training, and validation of computer-assisted detection/diagnosis software

et al. 2020

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Purpose To build a novel, open-source, purely web-based platform system to address problems in the development and clinical use of computer-assisted detection/diagnosis (CAD) software. The new platform system will replace the existing system for the development and validation of CAD software, Clinical Infrastructure for Radiologic Computation of United Solutions (CIRCUS). Methods In our new system, the two top-level applications visible to users are the web-based image database (CIRCUS DB; database) and the Docker plug-in-based CAD execution platform (CIRCUS CS; clinical server). These applications are built on top of a shared application programming interface server, a three-dimensional image viewer component, and an image repository. Results We successfully installed our new system into a Linux server at two clinical sites. A total of 1954 cases were registered in CIRCUS DB. We have been utilizing CIRCUS CS with four Docker-based CAD plug-ins. Conclusions We have successfully built a new version of the CIRCUS system. Our platform was successfully implemented at two clinical sites, and we plan to publish it as an open-source software project.

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“…They reported a sensitivity of 92.3%, which is in line with the detection performance we found for the comparable group of T1 tumors. Earlier this year, Vassallo et al compared unassisted and cloud-based CAD of pulmonary nodules in patients with extrathoracic malignancy [13]. A total of 215 lung nodules with a diameter between 3 and 28 mm in 75 patients were used for evaluation.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors

Weikert

D’Antonoli

Bremerich

et al. 2019

Contrast Media & Molecular Imaging

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Automated detection and segmentation is a prerequisite for the deployment of image-based secondary analyses, especially for lung tumors. However, currently only applications for lung nodules ≤3 cm exist. Therefore, we tested the performance of a fully automated AI-based lung nodule algorithm for detection and 3D segmentation of primary lung tumors in the context of tumor staging using the CT component of FDG-PET/CT and including all T-categories (T1–T4). FDG-PET/CTs of 320 patients with histologically confirmed lung cancer performed between 01/2010 and 06/2016 were selected. First, the main primary lung tumor within each scan was manually segmented using the CT component of the PET/CTs as reference. Second, the CT series were transferred to a platform with AI-based algorithms trained on chest CTs for detection and segmentation of lung nodules. Detection and segmentation performance were analyzed. Factors influencing detection rates were explored with binominal logistic regression and radiomic analysis. We also processed 94 PET/CTs negative for pulmonary nodules to investigate frequency and reasons of false-positive findings. The ratio of detected tumors was best in the T1-category (90.4%) and decreased continuously: T2 (70.8%), T3 (29.4%), and T4 (8.8%). Tumor contact with the pleura was a strong predictor of misdetection. Segmentation performance was excellent for T1 tumors (r = 0.908, p<0.001) and tumors without pleural contact (r = 0.971, p<0.001). Volumes of larger tumors were systematically underestimated. There were 0.41 false-positive findings per exam. The algorithm tested facilitates a reliable detection and 3D segmentation of T1/T2 lung tumors on FDG-PET/CTs. The detection and segmentation of more advanced lung tumors is currently imprecise due to the conception of the algorithm for lung nodules <3 cm. Future efforts should therefore focus on this collective to facilitate segmentation of all tumor types and sizes to bridge the gap between CAD applications for screening and staging of lung cancer.

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A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies

Cited by 26 publications

References 38 publications

Contribution of CAD to the Sensitivity for Detecting Lung Metastases on Thin-Section CT – A Prospective Study with Surgical and Histopathological Correlation

Contribution of CAD to the Sensitivity for Detecting Lung Metastases on Thin-Section CT – A Prospective Study with Surgical and Histopathological Correlation

Novel platform for development, training, and validation of computer-assisted detection/diagnosis software

Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors

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