Can radiological technologists serve as primary screeners of low-dose computed tomography for the diagnosis of lung cancer?

Arai, Takaaki; Nagashima, Chihiro; Muramatsu, Yoshihisa; Murao, Kohei; Yamaguchi, Isao; Ushio, N; Hanai, Kouzou; Kaneko, Masahiro

doi:10.3233/xst-180409

Cited by 1 publication

(1 citation statement)

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“…However, the new case-based CAD schemes do not directly compete with tumor-based CAD schemes. For example, although case-based CAD schemes may not be used as "a second reader" as the current tumor-based CAD schemes, they have the potential to be used as prescreening tools to help stratify image cases into high-and low-risk groups (e.g., like prescreening performed by technologists [55]). Using the model-generated prediction scores (or "warning" signs), radiologists can focus on reading and interpreting higher risk cases to increase detection sensitivity by reducing the risk of missing or overlooking subtle tumors, while reducing image reading time in lower risk cases.…”

Section: Discussionmentioning

confidence: 99%

Developing global image feature analysis models to predict cancer risk and prognosis

Qiu

Aghaei

Mirniaharikandehei

et al. 2019

Vis. Comput. Ind. Biomed. Art

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In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

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Section: Discussionmentioning

confidence: 99%

Developing global image feature analysis models to predict cancer risk and prognosis

Qiu

Aghaei

Mirniaharikandehei

et al. 2019

Vis. Comput. Ind. Biomed. Art

View full text Add to dashboard Cite

show abstract

Can radiological technologists serve as primary screeners of low-dose computed tomography for the diagnosis of lung cancer?

Abstract: In order for accredited Radiological Technologists to serve as primary screeners of low-dose computed tomography, it is important to revise the educational system according to current standard practices.

Cited by 1 publication

References 7 publications

Developing global image feature analysis models to predict cancer risk and prognosis

Developing global image feature analysis models to predict cancer risk and prognosis

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