128 Chapter 7 Specificity to predict patients unlikely to achieve a (near-)complete response was relatively high (ranging between 68-73%) and considerably higher than the sensitivity of only 49-59% to predict which patients would become (near-)complete responders. These results indicate that the study readers were better at estimating patients likely to end up with residual tumor. We hypothesize that recognizing the really “ugly” tumour cases (unlikely to ever reach organ preservation) may be relatively straightforward, while there is a more broad spectrum of “intermediate risk” cases where it will be more challenging to predict which patients will proceed to show a good response versus a (near-) complete response to treatment. Interestingly, our results are also more or less in line with previous reports on assessing response in the restaging setting after completion of CRT where radiologists are generally also better at identifying poor responders than in identifying complete (or near-complete) responders[21-23]. Ultimately, the selection of patients for organ-preservation should not be based on imaging only, but informed by a combination of MRI, clinical (digital rectal) examination, and endoscopy [3,24]. Of note, our current results are based solely on “simple” visual morphologic assessment and baseline staging of anatomical MR images by radiologists, without the need for additional quantitative measurements, advanced (functional) imaging sequences or computational algorithms. The benefit of such an approach is that it can easily be implemented in daily practice and is relatively comprehensive for clinicians. An important drawback, however, is that it is also observer dependent and influenced by the experience level of radiologists, as also reflected by our results that show a tendency towards higher IOA and diagnostic performance for the more experienced study readers. Though we aimed to provide readers with clear scoring instructions (see Fig 1 and 2), criteria such as ‘obvious nodal involvement’ and ‘bulky tumor’ remain subjective criteria, which probably contributed to the relatively low IOA. These effects are less of an issue when using more quantitative or AI-based methods, which have formed a major topic of research in recent literature. Functional imaging parameters such as the Apparent Diffusion Coefficient (ADC) derived from diffusion-weighted MRI, and perfusion metrics (e.g. K-trans) derived from dynamic contrast-enhanced MRI, have all shown potential as pre-treatment predictors of response[5,25]. In addition, “texture” features such as entropy and uniformity that reflect tissue heterogeneity have been associated with the chance of successful tumor response[15,16,26]. When combining such quantitative features in multivariable (radiomics) models, published reports have shown varying AUCs ranging between 0.68-0.97 to predict rectal tumor response at baseline[27]. Van Griethuysen et al. showed that the predictive performance of a quantitative AI model was similar to that of a visual morphologic response prediction performed by experienced radiologists[17]. Other studies have shown a complementary value for AI (radiomics) and visual morphologic evaluations and have demonstrated that combining these two approaches can increase diagnostic performance to predict response[17,27-30]. Nevertheless, reported
RkJQdWJsaXNoZXIy MjY0ODMw