TY - GEN
T1 - False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms
AU - Gandomkar, Ziba
AU - Lewis, Sarah J.
AU - Siviengphanom, Somphone
AU - Wong, Dennis
AU - Ekpo, Ernest U.
AU - Suleiman, Moayyad E.
AU - Tao, Xuetong
AU - Reed, Warren
AU - Brennan, Patrick C.
PY - 2023
Y1 - 2023
N2 - The global radiomic signature extracted from mammograms can indicate that malignancy appearances are present within an image. This study focuses on a set of 129 screen-detected breast malignancies, which were also visible on the prior screening examinations (i.e., missed cancers based on the priors). All cancer signs on the prior examinations were actionable based on the opinion of a panel of three experienced radiologists, who retrospectively interpreted the prior examinations (knowing that a later screening round had revealed a cancer). We investigated if the global radiomic signature could differentiate between screening rounds: when the cancer was detected ("identified cancers"), from the round immediately before ("missed cancers"). Both identified cancers and "missed cancers"were collected using a single vendor technology. A set of "normals", matched based on mammography units, was also retrieved from a screening archive. We extracted a global radiomic signature, containing first and second-order statistics features. Three classification tasks were considered: (1) "identified cancers"vs "missed cancers", (2) "identified cancers"vs "normals", (3) "missed cancers"vs "normal". To train and validate the models, leave-one-case-out cross-validation was used. The classifier resulted in an AUC of 0.66 (95%CI=0.60-0.73, P<0.05) for "missed cancers"vs "identified cancers"and an AUC of 0.65 (95%CI=0.60-0.69, P<0.05) for "normals"vs "identified cancers". However, the AUC of the classifier for differentiating "normals"from "missed cancers"was at chance-level (AUC=0.53 (95%CI=0.48-0.58, P=0.23). Therefore, eliminating some of these "missed"cancers in clinical practice would be very challenging as the global signal of the malignancy that help with a diagnosis, are at best weak.
AB - The global radiomic signature extracted from mammograms can indicate that malignancy appearances are present within an image. This study focuses on a set of 129 screen-detected breast malignancies, which were also visible on the prior screening examinations (i.e., missed cancers based on the priors). All cancer signs on the prior examinations were actionable based on the opinion of a panel of three experienced radiologists, who retrospectively interpreted the prior examinations (knowing that a later screening round had revealed a cancer). We investigated if the global radiomic signature could differentiate between screening rounds: when the cancer was detected ("identified cancers"), from the round immediately before ("missed cancers"). Both identified cancers and "missed cancers"were collected using a single vendor technology. A set of "normals", matched based on mammography units, was also retrieved from a screening archive. We extracted a global radiomic signature, containing first and second-order statistics features. Three classification tasks were considered: (1) "identified cancers"vs "missed cancers", (2) "identified cancers"vs "normals", (3) "missed cancers"vs "normal". To train and validate the models, leave-one-case-out cross-validation was used. The classifier resulted in an AUC of 0.66 (95%CI=0.60-0.73, P<0.05) for "missed cancers"vs "identified cancers"and an AUC of 0.65 (95%CI=0.60-0.69, P<0.05) for "normals"vs "identified cancers". However, the AUC of the classifier for differentiating "normals"from "missed cancers"was at chance-level (AUC=0.53 (95%CI=0.48-0.58, P=0.23). Therefore, eliminating some of these "missed"cancers in clinical practice would be very challenging as the global signal of the malignancy that help with a diagnosis, are at best weak.
UR - https://hdl.handle.net/1959.7/uws:74288
U2 - 10.1117/12.2655154
DO - 10.1117/12.2655154
M3 - Conference Paper
SN - 9781510660397
BT - Proceedings of SPIE: Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, 21-23 February 2023, San Diego, California, United States
PB - SPIE
T2 - Medical Imaging (Conference : SPIE)
Y2 - 21 February 2023
ER -