12 research outputs found

    Visual discrimination of screen-detected persistent from transient subsolid nodules: An observer study - Fig 3

    Get PDF
    <p><b>(a)</b> Correctly identified transient lesion with a probability score of ≤ 40 by all four observers. <b>(b)</b> Correctly identified persistent lesion with a probability score of ≥ 80 by all four observers. <b>(c)</b> Incorrectly identified lesion by majority of observers: transient lesion, but scored as persistent (probability score ≥ 60). <b>(d)</b> Incorrectly identified lesion by majority of observers: persistent lesion, but scored as transient (probability score ≤ 40).</p

    Univariate analyses.

    No full text
    <p>Table describes morphological features with at least 2 observers in which the feature is seen significantly different between transient (T) and persistent (P) subsolid nodules using Chi-square. The total number of included nodules after exclusion is 172.</p

    Visual discrimination of screen-detected persistent from transient subsolid nodules: An observer study - Fig 4

    No full text
    <p><b>(a)</b> A transient lesion with disagreement (2 versus 2) among observers. <b>(b)</b> A persistent lesion with disagreement (2 versus 2) among observers.</p

    Reading workstation.

    No full text
    <p>The morphological features to be scored are listed on the left side of the monitor display. Lower-left corner has two text fields to enter the probability (0–100) and any comments. A magnified axial view of the nodule under evaluation is centered in the middle of the display. Coronal/sagittal projections are available on the right side of the screen, display size of the three projections was interchangeable. Processing tools such as windowing and magnification as well the full 3D CT dataset were available at any time.</p

    Performance of observers and PanCan model.

    No full text
    <p>ROC curves of the observers 1–11, the PanCan model 2b, the PanCan model 1b, and only nodule size as predictor for (A) discriminating randomly selected benign nodules from malignant nodules on the left, and (B) on the right discriminating size-matched benign nodules from malignant nodules. Note that in Fig 1A the PanCan model outperforms human observers at a specificity > 80%, while in Fig 1B all human observers perform better than the PanCan model.</p

    Examples of nodules with morphological characteristics uniformly scored by six or seven observers.

    No full text
    <p>Every nodule is displayed in axial plane. Images show a field of view of 60 x 60 mm, in which the nodule is centered. From left to right: A) Solid malignant nodule, 13 mm, with spiculation; B) Part-solid malignant nodule, 17 mm, with retraction of a fissure; C) Solid malignant nodule, 30 mm, with distortion of surrounding architecture; D) Solid benign nodule, 11 mm, with a well-defined border.</p

    Observer disagreement for malignancy probability score.

    No full text
    <p>Examples of nodules for which the PanCan model and the observers showed conflicting malignancy probability scores. For the observers, a threshold of < 25% averaged over all observers was considered a 'low' score, and a threshold of > 60% was considered a 'high' score. For the PanCan model, a threshold of < 6% was considered a 'low' score and a threshold of > 30% was considered a 'high' score. Nodules are displayed in axial plane. From left to right: A) Solid <i>malignant</i> nodule, 15 mm, observers scored 24%, PanCan 35%; B) Pure ground-glass <i>malignant</i> nodule, 9 mm, observers scored 58%, PanCan 4%; C) Solid <i>benign</i> nodule, 16.5 mm, observers scored 14%, PanCan 37%; D) Part-solid <i>benign</i> nodule, 13 mm, observers scored 65%, PanCan 14%.</p
    corecore