Several techniques for combined CT-window settings have been described in the past, but none of these methods has ever found its way into clinical practice. First studies on this topic date from the early years of CT. Already in the 1980’s, Lehr et al. proposed a technique of histogram equalization with adjustment of image intensities to enhance contrast . Some years later, Gomori et al. described a technique with non-linear window . Almost 2 decades after the publication of Lehr, Fayad and colleagues studied a new method called ‘adaptive histogram equalization’, based on the initial histogram equalization technique. They investigated the usefulness of the technique in a clinical setting and were able to show a significant reduction in interpretation time for the combined window setting, but overall accuracy was insufficient for replacement of the conventional window settings by the new combined window . A new technique of companding CT-images was published in 2011 by Cohen-Duwek et al., with limited clinical data . More recently, Mandell et al. investigated feasibility of a ‘blended’ CT-window in patients with thoracic trauma . Their results showed that the blended window approach allows for faster preliminary interpretation of axial chest CT-scans of trauma patients, with no significant difference in diagnostic performance as compared to conventional window settings. The same authors illustrated the use of window blending in aggressive infections, metastatic cancer and penetrating trauma .
Unfortunately, these investigations on clinical use of combined CT-window settings are limited in scope and number of patients examined.
If we want to establish the feasibility of using a combined CT-window, for example in patients with lung cancer, this would require an analysis of lesion detection, lesion measurement and lesion characterization. It is with this in mind that we decided to explore the possibilities of the AIO-window in this patient population. It has been previously shown that lesion detection on a single AIO-window is at least as good as on multiple conventional window settings . However, lesion management measurement and characterization are equally important parameters in the diagnosis and follow-up of oncology patients. Therefore, in this study, we investigated the aspect of lesion measurement. Evaluation of response remains size-dependent and anatomic measurements are a crucial element in the evaluation of oncology studies . Since clinical decisions are often based on CT measurements, accuracy and reproducibility of these measurements, with low rates of intra- and interobserver variability, are vital. It is known that multiple factors contribute to the variability of measurements, including operator dependant and technical aspects [12,13,14,15,16,17,18].
In this study, using retrospective data, we evaluated if lesion measurement on a combined AIO-window is comparable to lesion measurement on conventional window settings. Readers were asked to measure 368 lesions, twice on the AIO-window and twice on the conventional window settings. Overall intra-and interobserver agreement values were consistently high for both windows. Although still excellent, intra-observer agreement was slightly higher for the conventional window setting whereas interobserver agreement was slightly higher for the AIO-window. Subanalysis according to reader experience, showed no trend and confidence intervals were overlapping. Agreement is excellent for all categories for AIO as well as conventional window settings. Our data do not support that agreement is better for experienced radiologists. This suggests that radiologists with different level of expertise can safely perform CT measurements and that this is also the case for the novel AIO-window.
It is generally accepted that measurement differences are greater in lesions that are irregular and poorly defined or when the edge of the lesion is irregular or spiculated [16, 19]. Both intra- and interobserver agreement was lower for ill-defined lesions compared to well-defined lesions. The effect was however minimal, with still excellent agreement, on the AIO-window and conventional window settings for both categories. The Bland-Altman plots on comparing mean diameter on the AIO and conventional window settings show that lesions outside the 95% limits of agreement are mostly ill-defined lesions (red dots).
Since lesions in lung parenchym and lung window settings have a different visual aspect on the AIO-window (Figs. 1 and 2) than lesions in soft tissue window settings, this was evaluated separately. As one might intuitively expect, both intra- and interobserver agreement on the AIO-window was better for the soft tissue lesions, compared to the lesions that were evaluated in lung window setting. The agreement however was still excellent. For combined evaluation of ill- or well-defined lesions and lung or soft window on the AIO-window, agreement for ill-defined lesions in lung window seems contradictory slightly better than well-defined lesions in lung window. This is probably an artefact related to the fact that the majority of well-defined lesions in lung window are small (26/49) and medium sized (23/49) whereas the category of ill-defined lesions in lung window contains mainly medium (32/85) and large size (28/85) lesions for which overall agreement is better than for the small lesions.
In general, measurement variability increases as lesion size decreases, with the greatest variability in small tumor measurements . Our data confirm this effect but show no difference for the AIO-window. For both windows, intra-and interobserver variability is lowest for lesions smaller than 10 mm (with moderate agreement) and highest for lesions of 20 mm or larger (excellent agreement). Comparing mean diameter on the AIO-window with conventional window settings show that the 95% limits of agreement are much wider for smaller lesions compared to large lesions.
Comparison of mean diameter of lesions on the AIO-window with conventional window settings, shows a consistent smaller diameter on the AIO-window. Although this difference is only 3.4%, it is statistically significant. This correlates with a diameter difference of 0.6 mm, making the clinical impact probably less important. These findings suggest that lesion measurement and comparison should be performed on the same window when comparing images and deciding on the response. A possible explanation for the overall smaller diameter on the AIO-window may lie in the fact that lesions in lung parenchyma on the conventional window settings were measured in lung window setting with a hard kernel. The AIO-window algorithm uses the soft kernel images and therefore the edges of pulmonary nodules and masses may be less sharp which may impact measurement. When one might use this window in clinical practice, it should be advised not to compare measurements on the AIO-window with previous measurement on conventional window settings.
Although it was not the primary goal of our study, our data also give insight in measurement variability on conventional window settings: overall intra- and interobserver variability is excellent. Subanalysis, taking into account lesion sharpness and size, shows excellent agreement for both well- and ill-defined lesions as well as larger lesions (≥ 20 mm). For lesions between 10 and 19 mm agreement is good, for small lesions agreement is moderate. Although still excellent, agreement is slightly higher for well-defined compared to ill-defined lesions. This is what one may instinctively expect and has been demonstrated by other studies [16, 19]. In a large study with 17 radiologists, McErlean investigated intra- and interobserver variability in CT measurements in oncology . Overall variability rates as well as subanalysis showing that smooth margin and larger lesion size reduce measurement variability, are within the same range in our study. Image selection is a crucial element in lesion measurement. Lesions are generally not round but have more irregular shapes. Hopper et al. showed a wide variability between different observers in their selection of metastatic foci for measurement with significant error rate in irregular and poorly defined tumors . Selection of the largest long-axis diameter can differ depending on the axial slice selected. An important limitation of the study by McErlean et al. was that readers were presented with preselected images . The strength of our study is that lesions were marked at the top of the lesion and readers had to select the axial slice on which they thought was the largest diameter. Taking this into account one might expect an agreement that is less good. This was however not the case.
There are several limitations to our study. First and foremost, is the absence of an ‘imaging ground truth’ for lesion size. For obvious reasons, we were not able to compare lesion sizes with surgical pathology specimens because most patients presented with metastatic disease and were not operated. Second, our study was exclusively performed on contrast-enhanced CT’s. While this doesn’t matter for measurement of lesions in lung window settings, lesion measurement in soft tissue window settings is far more difficult. Third, we have chosen a highly specific patient population of thoracic oncology patients, because the AIO-window was specifically designed with such patients in mind. Hence, our analysis included only lesions in chest and upper abdomen. We focused in our study on 3 mm slices, since these are often used in daily practice for follow-up of thoracic oncology studies, for which the AIO-window in particular has been designed. Although many lesions in our study (Table 1) were pulmonary nodules, primarily metastatic, the AIO-window should not be used for dedicated follow-up of solitary pulmonary nodules. These lesions should be measured on thin slices (e.g. 1 mm) in lung kernel. Furthermore, in the evaluation of part-solid nodules, evaluation of the solid aspect of the lesions is crucial for staging. Currently the AIO algorithm is not designed for dedicated evaluation of pulmonary nodules. Further research in this field is necessary, before implementation of the AIO-window for evaluation of solitary pulmonary nodules, both solid and subsolid. Last but not least, a single radiologist selected lesions and addressed the category of sharpness of lesions (which is subjective), creating some bias. CT-studies and therefore lesions were not assessed in random order during the different reading sessions. Although this might create some recall bias, we believe this is minimal because of the high number of lesions (368). To minimize bias on recognition of lesion morphology (in particular for lung parenchymal lesions), measurements were first performed on the AIO-window and subsequently on conventional window settings. Some readers were familiar with the AIO-window from the previous clinical study on lesion detection . There was however a one-year time interval between both studies, making visual image recall less important.