Improving predictive accuracy of early recurrence in pancreatic ductal adenocarcinoma: Role of postoperative serum tumor markers

Abstract

In a recent study by He et al, the nomogram integrates postoperative serum tumor markers such as carbohydrate antigen 19-9 and carcinoembryonic antigen, thereby improving the accuracy of identifying high-risk patients compared to relying solely on preoperative markers, which has significant implications for customizing adjuvant therapy and potentially improving outcomes for this aggressive form of cancer. However, the study’s single-center design and short follow-up period may limit the generalizability of its findings and potentially introduce reporting bias. Future studies could consider additional confounding factors, such as adjuvant chemotherapy and variations in surgical techniques, to improve the model’s accuracy. Furthermore, it would be valuable to validate the nomogram in broader, prospective cohorts and explore the inclusion of additional markers like circulating tumor DNA to refine further its predictive power and applicability across diverse patient populations.

Key Words: Adjuvant therapy; Adenocarcinoma; Early recurrence; Nomogram; Postoperative; Prognosis; Pancreatic cancer; Tumor marker

Core Tip: A study by He et al published a serum tumor markers-based nomogram to predict early recurrence in patients with pancreatic ductal adenocarcinoma following radical surgery. This model incorporates markers such as carbohydrate antigen 19-9 and carcinoembryonic antigen, which could significantly improve the accuracy of recurrence prediction compared to using only preoperative markers. The nomogram provides a valuable tool for categorizing patients and guiding the use of adjuvant therapy, addressing an important need in the management of this aggressive form of cancer. While the findings are promising, validating the model further using diverse and larger patient cohorts and considering including additional factors to maximize its clinical relevance and applicability is recommended.

TO THE EDITOR

He et al[1] aimed to establish an improved nomogram based on postoperative serum tumor markers (STMs) to predict early recurrence (ER) in pancreatic ductal adenocarcinoma (PDAC). While acknowledging the authors’ rigorous efforts and valuable contributions, we offer constructive suggestions for further refinement. The research presented important progress in predicting ER in patients with PDAC following curative surgery, utilizing a postoperative STMs-based nomogram. Incorporating postoperative STMs like carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen into the predictive model showed a noticeable improvement in identifying high-risk ER patients compared to previous approaches that relied only on preoperative markers[2].

PDAC remains one of the most aggressive cancers[3], with the majority of recurrences occurring within the first year following surgery[4]. As noted in the study, predicting ER is significant for guiding adjuvant therapy decisions, potentially improving patient outcomes[1]. The authors’ nomogram, which integrates postoperative STMs, offers a more precise tool for stratifying patients based on their risk of recurrence. Using postoperative markers like CA19-9 and carcinoembryonic antigen provides a valuable advantage in customizing treatment approaches in clinical settings[5]. The research emphasizes that while preoperative tumor markers, especially CA19-9, have been identified as prognostic indicators, postoperative assessments provide more accurate details about remaining disease and tumor activity following surgery. This is important because the timing of cancer recurrence significantly impacts overall survival and treatment effectiveness.

When considering the findings of a single-center study, it is important to be mindful of the limitations. Conclusions drawn from such studies may not be universally applicable, as they may not reflect the broader population or diverse clinical contexts[6]. Variations in surgical approaches, patient demographics, and post-surgical management across different healthcare facilities can impact the generalizability of the results. Furthermore, more comprehensive potential confounding factors should be integrated. The authors have commendably adjusted for baseline covariates. The Cox regression analysis did not include important confounding factors such as adjuvant chemotherapy use, surgical technique variations, or patient comorbidities. These factors can potentially skew the correlation between postoperative STMs and ER, thereby introducing bias into the findings[7]. Furthermore, the duration of follow-up in the study needed to be longer to capture all cases of recurrence or long-term survival outcomes. Short follow-up periods could underreport late recurrences, limiting our understanding of the nomogram’s ability to predict long-term outcomes.

The study focused more on positive results while not fully addressing limitations or less favorable outcomes. This reporting bias could result in an overestimation of the effectiveness of the nomogram, potentially distorting the study’s perceived impact[8]. Additionally, the current study did not compare the STM-based nomogram to other available predictive models for ER in PDAC. Without this comparison, it is challenging to determine whether this nomogram represents a significant improvement over existing tools or methods. Similarly, the sensitivity and specificity of the current model may be further improved by including additional factors. Furthermore, variations in surgical techniques, margin status, and pathological evaluation, such as lymph node assessment, may have influenced recurrence risk, but these factors may not have been standardized across the cohort. Differences in surgical quality or pathology reporting could introduce variability that impacts the accuracy of recurrence predictions[9].

In the context of future research, we want to share a few points for consideration. Firstly, while the nomogram has shown promising accuracy, it would be valuable to conduct validation of the current model in a larger prospective study to ascertain its effectiveness across diverse patient populations and surgical centers. Secondly, additional postoperative markers, such as circulating tumor DNA, could support the model’s predictive capacity, particularly in identifying minimal residual disease[10]. Lastly, studies into the interplay between postoperative STMs and other clinical variables, such as the response to neoadjuvant therapy or the molecular subtypes of PDAC[11], may lead to better and deeper insights into personalized adjuvant treatment strategies.

In conclusion, the recent study by He et al[1] is an important advance in our ability and their valuable contribution to this area. The postoperative STM-based nomogram marks a significant advancement in improving the management of PDAC by providing more precise predictions of ER. Our suggestions aim to refine already outstanding research, and we look forward to witnessing further insightful works from the authors.