PMC:2940044 / 1441-4725 JSONTXT

{"project":"TEST0","denotations":[{"id":"20532890-181-186-47753","span":{"begin":194,"end":195},"obj":"[\"18832239\", \"16569779\", \"16794150\"]"},{"id":"20532890-76-81-47754","span":{"begin":276,"end":277},"obj":"[\"17409321\"]"},{"id":"20532890-79-84-47755","span":{"begin":279,"end":280},"obj":"[\"15358846\"]"},{"id":"20532890-238-243-47756","span":{"begin":545,"end":546},"obj":"[\"19527363\", \"17114540\"]"},{"id":"20532890-160-165-47757","span":{"begin":863,"end":864},"obj":"[\"17209117\"]"},{"id":"20532890-111-117-47758","span":{"begin":1126,"end":1128},"obj":"[\"12616008\", \"10572847\"]"},{"id":"20532890-125-131-47759","span":{"begin":1259,"end":1261},"obj":"[\"16244252\", \"11895300\", \"15917444\", \"10796939\"]"},{"id":"20532890-39-45-47760","span":{"begin":1494,"end":1496},"obj":"[\"18353630\"]"},{"id":"20532890-43-49-47761","span":{"begin":1498,"end":1500},"obj":"[\"17386998\"]"},{"id":"20532890-184-190-47762","span":{"begin":2811,"end":2813},"obj":"[\"12616008\"]"}],"text":"Introduction\nComputer-aided detection (CAD) was introduced in breast cancer screening as a technology to avoid perceptual oversights and its effectiveness has been demonstrated in many studies [1–3]. Nevertheless, there is a continuing debate regarding the usefulness of CAD [4, 5]. While most radiologists agree that CAD systems have value because of their high performance in detecting microcalcifications, many believe that current CAD algorithms for masses and architectural distortions have too many false positives to allow effective use [6–8]. Evidently, more research is needed to improve CAD algorithms. However, the lack of confidence some radiologists have in CAD may also be another reason. In previous research strong evidence was found that the performance of CAD algorithms may not be a problem, but that the concept of CAD may need to be revised [9]. The assumption on which CAD is currently based is that significant lesions initially missed by radiologists will be acted upon when CAD marks them. In practice, however, many lesions are not missed by perceptual oversight but due to incorrect interpretation [10–12]. Therefore, it is not surprising that studies reveal that many significant lesions are still missed even when CAD marks them [13–16]. To prevent such interpretation errors CAD needs to be designed to help radiologists with decision making.\nThe purpose of this study was to investigate a novel way of using CAD algorithms. In the traditional prompting approach [17, 18], CAD results are displayed after the reading is completed, offering the reader a possibility to check if no perceptual failures occurred related to search. In current practice, readers are strongly discouraged to downgrade their findings on the basis of CAD. Compared with the traditional approach, we investigated a method in which CAD marks are only displayed on request during the reading. This novel approach means that when the reader is inspecting a certain region in a mammogram, that particular region can be probed for the presence of any CAD information using a pointer and, if present, only the CAD information about this location is shown. In addition to the CAD mark also the level of suspicion computed by the CAD system is displayed. However image regions deemed normal by the reader are not probed for CAD and thus no other CAD marks elsewhere on the image would be shown. Obviously, this approach will not aid in avoiding perceptual oversights. However, this method has the potential to aid readers in making decisions when they inspect potential lesions, without being distracted by false positives of CAD.\nOur study was motivated by previous research, which demonstrated a significant improvement in detection performance when CAD mass marks were independently combined with reader scores [10]. In that study, CAD marks on regions not reported by the reader were not used, which is similar to the approach investigated here. As independent combination of reader results with CAD would not be easily accepted in clinical practice, we designed a screening workstation in which readers themselves can combine their interpretation with CAD in an interactive way. To investigate the proposed CAD concept, we conducted a reader study in which nine readers participated."}

{"project":"0_colil","denotations":[{"id":"20532890-17409321-47754","span":{"begin":276,"end":277},"obj":"17409321"},{"id":"20532890-15358846-47755","span":{"begin":279,"end":280},"obj":"15358846"},{"id":"20532890-17209117-47757","span":{"begin":863,"end":864},"obj":"17209117"},{"id":"20532890-18353630-47760","span":{"begin":1494,"end":1496},"obj":"18353630"},{"id":"20532890-17386998-47761","span":{"begin":1498,"end":1500},"obj":"17386998"},{"id":"20532890-12616008-47762","span":{"begin":2811,"end":2813},"obj":"12616008"},{"id":"20532890-16569779-47753","span":{"begin":194,"end":195},"obj":"16569779"},{"id":"20532890-16794150-47753","span":{"begin":194,"end":195},"obj":"16794150"},{"id":"20532890-18832239-47753","span":{"begin":194,"end":195},"obj":"18832239"},{"id":"20532890-17114540-47756","span":{"begin":545,"end":546},"obj":"17114540"},{"id":"20532890-19527363-47756","span":{"begin":545,"end":546},"obj":"19527363"},{"id":"20532890-10572847-47758","span":{"begin":1126,"end":1128},"obj":"10572847"},{"id":"20532890-12616008-47758","span":{"begin":1126,"end":1128},"obj":"12616008"},{"id":"20532890-10796939-47759","span":{"begin":1259,"end":1261},"obj":"10796939"},{"id":"20532890-11895300-47759","span":{"begin":1259,"end":1261},"obj":"11895300"},{"id":"20532890-15917444-47759","span":{"begin":1259,"end":1261},"obj":"15917444"},{"id":"20532890-16244252-47759","span":{"begin":1259,"end":1261},"obj":"16244252"}],"text":"Introduction\nComputer-aided detection (CAD) was introduced in breast cancer screening as a technology to avoid perceptual oversights and its effectiveness has been demonstrated in many studies [1–3]. Nevertheless, there is a continuing debate regarding the usefulness of CAD [4, 5]. While most radiologists agree that CAD systems have value because of their high performance in detecting microcalcifications, many believe that current CAD algorithms for masses and architectural distortions have too many false positives to allow effective use [6–8]. Evidently, more research is needed to improve CAD algorithms. However, the lack of confidence some radiologists have in CAD may also be another reason. In previous research strong evidence was found that the performance of CAD algorithms may not be a problem, but that the concept of CAD may need to be revised [9]. The assumption on which CAD is currently based is that significant lesions initially missed by radiologists will be acted upon when CAD marks them. In practice, however, many lesions are not missed by perceptual oversight but due to incorrect interpretation [10–12]. Therefore, it is not surprising that studies reveal that many significant lesions are still missed even when CAD marks them [13–16]. To prevent such interpretation errors CAD needs to be designed to help radiologists with decision making.\nThe purpose of this study was to investigate a novel way of using CAD algorithms. In the traditional prompting approach [17, 18], CAD results are displayed after the reading is completed, offering the reader a possibility to check if no perceptual failures occurred related to search. In current practice, readers are strongly discouraged to downgrade their findings on the basis of CAD. Compared with the traditional approach, we investigated a method in which CAD marks are only displayed on request during the reading. This novel approach means that when the reader is inspecting a certain region in a mammogram, that particular region can be probed for the presence of any CAD information using a pointer and, if present, only the CAD information about this location is shown. In addition to the CAD mark also the level of suspicion computed by the CAD system is displayed. However image regions deemed normal by the reader are not probed for CAD and thus no other CAD marks elsewhere on the image would be shown. Obviously, this approach will not aid in avoiding perceptual oversights. However, this method has the potential to aid readers in making decisions when they inspect potential lesions, without being distracted by false positives of CAD.\nOur study was motivated by previous research, which demonstrated a significant improvement in detection performance when CAD mass marks were independently combined with reader scores [10]. In that study, CAD marks on regions not reported by the reader were not used, which is similar to the approach investigated here. As independent combination of reader results with CAD would not be easily accepted in clinical practice, we designed a screening workstation in which readers themselves can combine their interpretation with CAD in an interactive way. To investigate the proposed CAD concept, we conducted a reader study in which nine readers participated."}

{"project":"2_test","denotations":[{"id":"20532890-18832239-29356684","span":{"begin":194,"end":195},"obj":"18832239"},{"id":"20532890-16569779-29356684","span":{"begin":194,"end":195},"obj":"16569779"},{"id":"20532890-16794150-29356684","span":{"begin":194,"end":195},"obj":"16794150"},{"id":"20532890-17409321-29356685","span":{"begin":276,"end":277},"obj":"17409321"},{"id":"20532890-15358846-29356686","span":{"begin":279,"end":280},"obj":"15358846"},{"id":"20532890-19527363-29356687","span":{"begin":545,"end":546},"obj":"19527363"},{"id":"20532890-17114540-29356687","span":{"begin":545,"end":546},"obj":"17114540"},{"id":"20532890-17209117-29356688","span":{"begin":863,"end":864},"obj":"17209117"},{"id":"20532890-12616008-29356689","span":{"begin":1126,"end":1128},"obj":"12616008"},{"id":"20532890-10572847-29356689","span":{"begin":1126,"end":1128},"obj":"10572847"},{"id":"20532890-16244252-29356690","span":{"begin":1259,"end":1261},"obj":"16244252"},{"id":"20532890-11895300-29356690","span":{"begin":1259,"end":1261},"obj":"11895300"},{"id":"20532890-15917444-29356690","span":{"begin":1259,"end":1261},"obj":"15917444"},{"id":"20532890-10796939-29356690","span":{"begin":1259,"end":1261},"obj":"10796939"},{"id":"20532890-18353630-29356691","span":{"begin":1494,"end":1496},"obj":"18353630"},{"id":"20532890-17386998-29356692","span":{"begin":1498,"end":1500},"obj":"17386998"},{"id":"20532890-12616008-29356693","span":{"begin":2811,"end":2813},"obj":"12616008"}],"text":"Introduction\nComputer-aided detection (CAD) was introduced in breast cancer screening as a technology to avoid perceptual oversights and its effectiveness has been demonstrated in many studies [1–3]. Nevertheless, there is a continuing debate regarding the usefulness of CAD [4, 5]. While most radiologists agree that CAD systems have value because of their high performance in detecting microcalcifications, many believe that current CAD algorithms for masses and architectural distortions have too many false positives to allow effective use [6–8]. Evidently, more research is needed to improve CAD algorithms. However, the lack of confidence some radiologists have in CAD may also be another reason. In previous research strong evidence was found that the performance of CAD algorithms may not be a problem, but that the concept of CAD may need to be revised [9]. The assumption on which CAD is currently based is that significant lesions initially missed by radiologists will be acted upon when CAD marks them. In practice, however, many lesions are not missed by perceptual oversight but due to incorrect interpretation [10–12]. Therefore, it is not surprising that studies reveal that many significant lesions are still missed even when CAD marks them [13–16]. To prevent such interpretation errors CAD needs to be designed to help radiologists with decision making.\nThe purpose of this study was to investigate a novel way of using CAD algorithms. In the traditional prompting approach [17, 18], CAD results are displayed after the reading is completed, offering the reader a possibility to check if no perceptual failures occurred related to search. In current practice, readers are strongly discouraged to downgrade their findings on the basis of CAD. Compared with the traditional approach, we investigated a method in which CAD marks are only displayed on request during the reading. This novel approach means that when the reader is inspecting a certain region in a mammogram, that particular region can be probed for the presence of any CAD information using a pointer and, if present, only the CAD information about this location is shown. In addition to the CAD mark also the level of suspicion computed by the CAD system is displayed. However image regions deemed normal by the reader are not probed for CAD and thus no other CAD marks elsewhere on the image would be shown. Obviously, this approach will not aid in avoiding perceptual oversights. However, this method has the potential to aid readers in making decisions when they inspect potential lesions, without being distracted by false positives of CAD.\nOur study was motivated by previous research, which demonstrated a significant improvement in detection performance when CAD mass marks were independently combined with reader scores [10]. In that study, CAD marks on regions not reported by the reader were not used, which is similar to the approach investigated here. As independent combination of reader results with CAD would not be easily accepted in clinical practice, we designed a screening workstation in which readers themselves can combine their interpretation with CAD in an interactive way. To investigate the proposed CAD concept, we conducted a reader study in which nine readers participated."}

{"project":"MyTest","denotations":[{"id":"20532890-18832239-29356684","span":{"begin":194,"end":195},"obj":"18832239"},{"id":"20532890-16569779-29356684","span":{"begin":194,"end":195},"obj":"16569779"},{"id":"20532890-16794150-29356684","span":{"begin":194,"end":195},"obj":"16794150"},{"id":"20532890-17409321-29356685","span":{"begin":276,"end":277},"obj":"17409321"},{"id":"20532890-15358846-29356686","span":{"begin":279,"end":280},"obj":"15358846"},{"id":"20532890-19527363-29356687","span":{"begin":545,"end":546},"obj":"19527363"},{"id":"20532890-17114540-29356687","span":{"begin":545,"end":546},"obj":"17114540"},{"id":"20532890-17209117-29356688","span":{"begin":863,"end":864},"obj":"17209117"},{"id":"20532890-12616008-29356689","span":{"begin":1126,"end":1128},"obj":"12616008"},{"id":"20532890-10572847-29356689","span":{"begin":1126,"end":1128},"obj":"10572847"},{"id":"20532890-16244252-29356690","span":{"begin":1259,"end":1261},"obj":"16244252"},{"id":"20532890-11895300-29356690","span":{"begin":1259,"end":1261},"obj":"11895300"},{"id":"20532890-15917444-29356690","span":{"begin":1259,"end":1261},"obj":"15917444"},{"id":"20532890-10796939-29356690","span":{"begin":1259,"end":1261},"obj":"10796939"},{"id":"20532890-18353630-29356691","span":{"begin":1494,"end":1496},"obj":"18353630"},{"id":"20532890-17386998-29356692","span":{"begin":1498,"end":1500},"obj":"17386998"},{"id":"20532890-12616008-29356693","span":{"begin":2811,"end":2813},"obj":"12616008"}],"namespaces":[{"prefix":"_base","uri":"https://www.uniprot.org/uniprot/testbase"},{"prefix":"UniProtKB","uri":"https://www.uniprot.org/uniprot/"},{"prefix":"uniprot","uri":"https://www.uniprot.org/uniprotkb/"}],"text":"Introduction\nComputer-aided detection (CAD) was introduced in breast cancer screening as a technology to avoid perceptual oversights and its effectiveness has been demonstrated in many studies [1–3]. Nevertheless, there is a continuing debate regarding the usefulness of CAD [4, 5]. While most radiologists agree that CAD systems have value because of their high performance in detecting microcalcifications, many believe that current CAD algorithms for masses and architectural distortions have too many false positives to allow effective use [6–8]. Evidently, more research is needed to improve CAD algorithms. However, the lack of confidence some radiologists have in CAD may also be another reason. In previous research strong evidence was found that the performance of CAD algorithms may not be a problem, but that the concept of CAD may need to be revised [9]. The assumption on which CAD is currently based is that significant lesions initially missed by radiologists will be acted upon when CAD marks them. In practice, however, many lesions are not missed by perceptual oversight but due to incorrect interpretation [10–12]. Therefore, it is not surprising that studies reveal that many significant lesions are still missed even when CAD marks them [13–16]. To prevent such interpretation errors CAD needs to be designed to help radiologists with decision making.\nThe purpose of this study was to investigate a novel way of using CAD algorithms. In the traditional prompting approach [17, 18], CAD results are displayed after the reading is completed, offering the reader a possibility to check if no perceptual failures occurred related to search. In current practice, readers are strongly discouraged to downgrade their findings on the basis of CAD. Compared with the traditional approach, we investigated a method in which CAD marks are only displayed on request during the reading. This novel approach means that when the reader is inspecting a certain region in a mammogram, that particular region can be probed for the presence of any CAD information using a pointer and, if present, only the CAD information about this location is shown. In addition to the CAD mark also the level of suspicion computed by the CAD system is displayed. However image regions deemed normal by the reader are not probed for CAD and thus no other CAD marks elsewhere on the image would be shown. Obviously, this approach will not aid in avoiding perceptual oversights. However, this method has the potential to aid readers in making decisions when they inspect potential lesions, without being distracted by false positives of CAD.\nOur study was motivated by previous research, which demonstrated a significant improvement in detection performance when CAD mass marks were independently combined with reader scores [10]. In that study, CAD marks on regions not reported by the reader were not used, which is similar to the approach investigated here. As independent combination of reader results with CAD would not be easily accepted in clinical practice, we designed a screening workstation in which readers themselves can combine their interpretation with CAD in an interactive way. To investigate the proposed CAD concept, we conducted a reader study in which nine readers participated."}