Imaging-pathology correlation in pancreatic cancer: Methodological considerations and future directions

Abstract

A recent study by Luo et al examined the relationship between the pathological types of pancreatic cancer (PC) and their imaging characteristics. While this study presented an important step toward improving diagnostic accuracy for PC, we have several concerns regarding its generalizability, cohort selection, imaging variability, statistical methods, and potential confounding factors. We recommended that future research adopt multi-center, prospective designs to improve representation and minimize bias. Additionally, incorporating advanced imaging techniques such as radiomics and artificial intelligence and conducting more comprehensive statistical analyses would be valuable. By implementing these strategies, future studies can yield more reliable and externally validated findings that improve the clinical applicability of imaging-based differentiation of PC. Addressing these methodological issues could significantly advance the field of gastrointestinal oncology and improve patient management and outcomes.

Key Words: Pancreatic cancer; Imaging; Pathology; Computed tomography; Artificial intelligence; Magnetic resonance imaging; Endoscopic ultrasound; Diagnostic accuracy

Core Tip: A study by Luo et al examined the relationship between different pathological types of pancreatic cancer (PC) and their corresponding imaging features. This present study showed an advancement in improving the diagnostic accuracy for PC. However, to further improve the robustness and applicability of the findings, it is important to adopt a multi-center, prospective research design. Such an approach would provide better generalizability and representation among diverse patient populations. Additionally, integrating advanced imaging techniques, including radiomics and artificial intelligence-driven analyses, could significantly mitigate inconsistencies among different observers, thereby elevating the precision of diagnostics. While the findings are promising, future research would greatly benefit from using multivariable analyses and strategies to address missing data, which would help control for potential confounding factors, thus reinforcing the credibility of imaging-pathology correlations. Moreover, establishing external validation cohorts is important for verifying the predictive capabilities of these findings across various clinical settings and diverse patient demographics.

TO THE EDITOR

Pancreatic cancer (PC) is characterized by its aggressive nature and generally poor prognosis[1,2]. There is a significant need for effective prognostic targets to improve outcomes for patients diagnosed with PC. Cross-sectional imaging techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS), are important in the management of PC. Despite their importance, international guidelines for image-based stratification, prediction of treatment responses, and evaluation processes remain inconsistent and often inadequate. Pathological examination has long been regarded as the "gold standard" for disease diagnosis and characterization of PC. However, The accuracy of clinical pathological diagnoses is directly related to the experience of pathologists, which may introduce subjective variations, potentially resulting in a certain rate of misdiagnosis[3].

A recent study by Luo et al[4] provided a valuable understanding of PC-related challenges, focusing on the relationship between imaging characteristics and the various pathological types of PC. While the study is a valuable step forward, we have identified areas for improvement and offer constructive suggestions for further refinement.

First, study design, when evaluating the results of a single-center study, it is critical to admit its limitations. Conclusions drawn from this retrospective cohort from a single tertiary center may limit generalizability, as they may not fully represent broader populations or diverse clinical settings. Variations in patient demographics, healthcare accessibility, and disease prevalence in different populations could impact the applicability of these findings to other geographic groups. Future studies should include multi-center cohorts to improve the representativeness and external validity.

The study selection criteria, which required the availability of pre-treatment imaging modalities such as CT, MRIs, and EUS, likely contributed to creating a cohort of patients with a more advanced PC stage or those advantageous to access comprehensive diagnostic resources. This selection process, however, may not accurately represent the broader population of PC patients, particularly those who might only undergo one form of imaging due to financial or resource limitations. Moreover, the exclusion of individuals with a history of pancreatic surgery or those who have received neoadjuvant therapy could reduce patient diversity within the study. However, this reduction in heterogeneity might introduce confounding factors, as the imaging characteristics observed in untreated tumors may diverge significantly from those of treated tumors. While the interobserver agreement among the radiologists was predominantly high, specific imaging features exhibited considerable variability, particularly in assessing improvement patterns, which had an κ value of 0.79[5].

Additionally, the fact that the study relied solely on two radiologists from the same academic institution raises questions about the reproducibility of the results when applied to different clinical environments. To improve the reliability and consistency of these criteria in clinical practice, validating them with radiologists from various external institutions would be advantageous[5,6]. Despite using standardized imaging protocols, the decision to use a single model for each imaging modality - specifically a 3.0 Tesla MRI system - could restrict the generalizability of the findings. Variability in imaging quality and interpretation may arise from differences in equipment and the level of technician expertise, which suggests that the diagnostic criteria may require adaptation when utilized across different imaging systems[7]. Addressing these methodological limitations is important for supporting the study's conclusions and providing their applicability to broader clinical practices.

Second, statistical analysis and bias mitigation, it is important to incorporate more comprehensive statistical analyses alongside considerations for statistical power and potential confounding factors[8]. The authors should be commended for their efforts in adjusting for baseline covariates, demonstrating a thoughtful approach to their research. However, using multinomial logistic regression to examine the relationship between imaging features and pathological types of PC raises important concerns regarding confounding variables. Although certain imaging characteristics were found to correlate with specific pathological classifications, it is important to recognize that patient-related factors - such as age, gender, and existing health conditions - could obscure these associations. The authors failed to address missing data management during their analysis, which is vital to maintaining the integrity of research findings[9,10]. Without a clearly defined strategy for handling missing values, there is a risk of introducing bias, potentially undermining the reliability of the conclusions derived from the dataset[10]. Implementing multivariable analyses that account for these potential confounders would significantly enhance our understanding of how imaging features influence pathological classification. Considering methods such as propensity score matching or multiple imputation is beneficial to strengthen the analysis and address potential missing data issues, which could significantly improve the statistical rigor[11]. Furthermore, regression imputation is another helpful approach, as it uses a regression model to estimate missing values based on the relationships identified among the existing variables[12].

Third, overfitting and validation, while the sample size in this study is notably adequate, it is important to consider that subgroup analyses, particularly for rare tumor types, may lack sufficient statistical power. This limitation should be acknowledged, as it could affect the reliability of the findings about those subgroups. Furthermore, there is a risk that the statistical model used may be susceptible to overfitting, especially given the simultaneous testing of numerous imaging features across various cancer subtypes. Overfitting may compromise the model's generalizability to new patient data, which is necessary for clinical applicability[13]. Similarly, the study failed to account for temporal changes in imaging features among patients who underwent follow-up imaging. It would be beneficial to conduct internal or external validations of the models to provide their predictive accuracy and improve confidence in their clinical use to strengthen future research.

FUTURE RESEARCH

In the context of future research, we want to share a few points for consideration. First, a prospective study design utilizing a multi-center approach would be highly beneficial to improve the quality and applicability of the research, which would allow the recruitment of a more diverse patient population, thereby increasing the generalizability of the findings across different demographics and clinical settings. Moreover, prospective designs significantly mitigate the biases associated with missing or incomplete data, allowing for greater reliability in the outcomes and better control over confounding variables that may influence the results, leading to more accurate interpretations. Secondly, integrating advanced imaging techniques - such as radiomics and artificial intelligence (AI) - driven image analysis - holds immense potential for improving the precision with which pathologies can be differentiated[14]. Using AI, researchers could substantially decrease interobserver variability, resulting in more consistent and reproducible image interpretations. This technological advancement could significantly improve the diagnostic capabilities of healthcare professionals[14,15]. Thirdly, using multivariable regression models alongside longitudinal data collection would facilitate a more nuanced evaluation of how various patient demographics and co-morbid conditions impact the correlations between imaging features and pathological findings over time, which may contribute to the development of tailored treatment strategies that are more aligned with individual patient profiles. Fourth, to further support the reliability of the study, it would be cautious to establish an independent validation cohort, potentially sourced from an external institution, which would help to confirm the predictive power of imaging characteristics across different pathological subtypes, eventually improving the robustness of the research findings. Fifth, future research may integrate genetic and molecular data and detailed imaging characteristics to understand pancreatic neoplasms and their behavior better. By combining these diverse data types, researchers can uncover more profound insights into the biological underpinnings of these tumors, which may lead to more effective diagnostic, prognostic, and therapeutic strategies. Sixth, continued exploration may benefit from combining radiomics with AI using advanced machine learning techniques, particularly convolutional neural networks, to assess high-dimensional imaging features such as texture and shape[16]. Integrating data from multiple modalities and improving diagnostic accuracy could be notably improved.

Furthermore, the incorporation of explainable AI tools can improve the clinical relevance of the decision-making processes of these models, making them more applicable to radiologists and oncologists[17]. Finally, incorporating patient outcome data, particularly metrics such as progression-free survival and overall survival, would offer vital insights into the prognostic significance of distinct imaging-pathology correlations. Understanding how imaging features are associated with patient outcomes could be necessary for treatment planning and patient management in clinical settings.

CONCLUSION

In conclusion, this present study provided important insights into the use of imaging techniques for differentiating various subtypes of PC, which represented an advancement in tackling existing challenges in diagnosis. The study highlighted promising standardized imaging features in identifying PC; however, addressing the methodological and analytical concerns identified in this research for future studies is important. By doing so, we can improve the accuracy and applicability of imaging-based pathological differentiation in PC, which is vital for providing findings that can significantly impact clinical practice. Our recommendations are designed to refine what is already exceptional research, and we eagerly anticipate seeing further studies that will contribute to this critical field.