Diffusion-weighted magnetic resonance imaging combined with neutrophil-to-lymphocyte ratio predicts pathological response to neoadjuvant chemoradiotherapy in locally advanced esophageal squamous cell carcinoma

Introduction

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery has emerged as the standard treatment for patients with locally advanced esophageal cancer (1,2). However, Evidence suggests that the prognosis for patients who do not achieve a pathologic response to nCRT was historically less favorable. Given this, the ability to identify potential non-responders before initiating treatment becomes crucial (3-5). Thus, the prospect of upfront esophagectomy intervention gains significance as a viable strategy for non-responding patients, aiming to avoid the toxicity associated with nCRT. Precise differentiation between these two patient groups before neoadjuvant treatment is essential to facilitate personalized treatment for esophageal cancer. Existing studies suggest that conventional imaging techniques such as computed tomography (CT) and endoscopic ultrasound (EUS) lack sufficient accuracy in identifying pathological responders, exhibiting low sensitivity and specificity (6). The role of fluorodeoxyglucose (FDG) positron-emission in assessing response to nCRT in esophageal cancer remains inconclusive (6-8). In this context, diffusion-weighted imaging (DWI) emerges as a functional imaging technique capable of monitoring water proton movement between tissues, quantified by the apparent diffusion coefficient (ADC). The ADC measurement reflects the degree of free diffusion of water molecules within tissues, influenced by various diffusion barriers present in the microenvironment (9). Promising results have been observed in studies investigating the use of ADC for predicting pathologic response to neoadjuvant treatment in esophageal cancer. However, limited research has focused on the association between pretreatment ADC and pathologic response (10-14). Previous studies have highlighted the significance of cancer-related inflammation in cancer development and progression (15-17) and elevated pretreatment neutrophil-to-lymphocyte ratio (NLR) has been identified as a predictor of poor pathologic response after nCRT in esophageal cancer (18-21). Therefore, this study aims to explore the potential of baseline ADC and NLR as predictive markers for pathologic response to nCRT in patients with locally advanced esophageal squamous cell carcinoma (ESCC). We present this article in accordance with the STROBE reporting checklist (available at https://amj.amegroups.com/article/view/10.21037/amj-25-44/rc).

Methods

Participants

A retrospective analysis was conducted at Zhengzhou University Affiliated Cancer Hospital between December 2013 and July 2017 to investigate patients with esophageal cancer who underwent nCRT followed by surgical intervention. Eligible participants were required to meet the following inclusion criteria: (I) histological confirmation of esophageal squamous cell cancer, (II) age between 18 and 75 years, (III) Eastern Cooperative Oncology Group (ECOG) performance status score of 0 or 1, (IV) clinical stage T2-4aN0-3M0 according to the 7th edition of the UICC-TNM classification, (V) completion of the intended regimen of chemotherapy and radiation, and (VI) availability of pretreatment magnetic resonance imaging (MRI) scan data. Conversely, exclusion criteria encompassed patients with MRI contraindications, acute infections, or any hematologic disease.

Laboratory data

Venous blood samples were obtained from the participants within one week prior to the commencement of nCRT. The Sysmex XN-3000 Automated Hematology Analyzer (Sysmex Corporation, Kobe, Japan) was utilized to conduct a comprehensive count of peripheral neutrophils and lymphocytes. The peripheral NLR was calculated by dividing the neutrophil count by the lymphocyte count.

MRI technique

Participants underwent MRI scans, including DWI sequences, within one week before the initiation of neoadjuvant treatment. All MRI scans were conducted using a 3.0-T MRI system (Signal HDxt, GE Medical Systems, Waukesha, WI, USA) equipped with an eight-channel phased-array Torso coil, positioned according to the tumor location. The MRI sequences encompassed axial T1-weighted gradient-recall-echo, axial T2-weighted fast-spin-echo with cardiac and respiratory gating, and DWI using single-shot echo planar imaging with free-breathing. The DWI images were acquired with b-values of 0 and 600 s/mm².

Image analysis

Image quality assessment was performed by an experienced radiologist who was blinded to the clinical and histopathological data of the patients. The ADC values were calculated using function tool software at the GE AW4.4 workstation. ADC maps were generated from the DWI images with b-values of 0 and 600 s/mm². For ADC value quantification, region of interest (ROI) analysis was conducted by tracing ROIs along the lesion border on DWI images taken before nCRT, section by section. The ROIs were meticulously placed around the primary tumor while avoiding necrotic regions. The mean ADC values of the entire tumor were determined by averaging the tumor ROI ADC from each section.

Neoadjuvant treatment and surgery

The neoadjuvant chemotherapy regimen comprised weekly intravenous administration of paclitaxel (45 mg/m² body-surface area) and cisplatin (20 mg/m² body-surface area) for four weeks. Concurrently, a total dose of 40 Gy was delivered in 20 fractions (five fractions per week) over the course of four weeks, with treatment initiation on the first day of chemotherapy. Surgical intervention, involving esophagectomy with two-field lymphadenectomy, was performed approximately 3–4 weeks after the completion of nCRT.

Histopathologic analysis

Resection specimens were examined by experienced pathologists who were blinded to the MRI scans and laboratory data. The tumor regression grading (TRG) system proposed by Mandard et al. was employed for grading TRG scores (22). TRG 1 represented a pathological complete response (pCR) with the absence of residual cancer cells. TRG 2 indicated the presence of rare residual cancer cells scattered throughout fibrosis, while TRG 3 showed higher fibrosis than residual cancer. In TRG 4, the residual tumor was observed to outgrow fibrosis, and TRG 5 denoted no regressive changes. For analytical purposes, TRG scores 1–3 were categorized as pathological responders, while TRG 4–5 were designated as non-responders.

Statistical analysis

Differences in pre-treatment ADC values and tumor NLR between non-responders and responders were assessed using t-test or Mann-Whitney U-test. Univariate logistic regression analyses were conducted to identify independent predictors of tumor response. A combined predictive model was developed using logistic regression. The combined predictive index was calculated using the following logistic regression equation from our prediction model: P=e^(−0.659+2.118*ADC −1.044*NLR)/[1+ e^(−0.659+2.118*ADC −1.044*NLR)]. In the formula, the P values of each patient were calculated according to the predictive model equation. All statistical analyses were performed using SPSS software version 21.0. Receiver operating characteristic (ROC) curve analysis was employed to distinguish responders from non-responders using Med-Calc program software. The Kaplan-Meier method was employed to analyze survival outcomes, and differences were compared with log-rank tests. Overall survival (OS) was measured from the date of surgery until death from any cause or the last follow-up. Progression-free survival (PFS) started from the date of surgery until death or disease progression. Statistical significance was considered at a P value threshold of <0.05.

Ethical consideration

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Zhengzhou University Affiliated Cancer Hospital (No. 2015ct068). The retrospective nature of the study led to the exemption of the requirement for informed consent from individual patients.

Results

Patient characteristics

A total of 30 patients were included in the retrospective analysis for our study (Figure 1). Among these patients, the median age was 63 years (range, 45 to 71 years), and 16 patients were male. Of the 30 patients, 20 (66.7%) were classified as responders to nCRT (Table 1). Among these responders, 9 (30%) achieved a pathologic complete response (pCR). The remaining 10 patients (33.3%) were non-responders. Example of MRI scans from a patient with pathological response is presented (Figure 2).

Figure 1 Flowchart of the study population selection process. CRT, chemoradiotherapy; ESCC, esophageal squamous cell carcinoma; MRI, magnetic resonance imaging; nCRT, neoadjuvant chemoradiotherapy.

Figure 2 Patient with a cT4aN0M0 and showed a pathological response (TRG 2) to neoadjuvant chemoradiotherapy. The T2-weighted images, corresponding diffusion-weighted images, and corresponding ADC maps before (A-C) and after (D-F) nCRT were demonstrated. ADC, apparent diffusion coefficient; nCRT, neoadjuvant chemoradiotherapy; TRG, tumor regression grading.

Univariate analyses for the independent predictors

To evaluate the predictive capabilities of ADC and NLR, the optimal cut-off values for ADC and NLR were determined by ROC curve analysis, yielding area under the curves (AUCs) of 0.750 and 0.758, respectively. The optimal cut-off values were 1.86×10⁻³ mm²/s for ADC [sensitivity: 80.0%, specificity: 70.0%, positive predictive value (PPV): 84.2%, negative predictive value (NPV): 63.6%] and 2.31 for NLR (sensitivity: 75.0%, specificity: 80.0%, PPV: 88.2%, NPV: 61.5%) (Table 2). The rate of pN0 was not significantly different between the groups defined by high vs. low ADC [65% (13/20) vs. 35% (7/20), respectively; P>0.99], high vs. low NLR [40.0% (8/20) vs. 60% (12/20), respectively; P=0.71], or the favorable vs. unfavorable combined index [80% (16/20) vs. 20.0% (4/20), respectively; P=0.38]. The baseline characteristics of patients in different ADC, NLR and combined model groups are summarized in Table 3.

In the univariate analysis, both ADC (P=0.007) and NLR (P=0.004) were found to be significantly associated with pathological response. However, no significant differences were observed in age, sex, tumor length, tumor location, clinical T stages, or clinical N stages between responders and non-responders (Table 4).

Diffusion-weighted magnetic resonance imaging (DWI-MRI) combined with NLR parameters for predicting pathological responders

The assessment of pretreatment ADC, NLR and combined model revealed statistically significant differences between the responder and non-responder groups (P=0.02, P=0.007 and P=0.001, respectively) (Table 5). The ROC analyses demonstrated promising prediction capabilities for ADC and NLR, yielding AUC values of 0.750 (95% CI: 0.559–0.889) and 0.758 (95% CI: 0.567–0.894), respectively. To explore the potential complementary value of these markers, we constructed a logistic regression model to predict responders. The combined index of ADC and NLR exhibited the highest AUC of 0.840 (95% CI: 0.661–0.948) compared to their individual values, as illustrated in Figure 3. The optimal cut-off value for the combined index, as determined by the Youden index, was 0.48. At this threshold, the model demonstrated a sensitivity of 95.0%, a specificity of 70.0%, a PPV of 86.4%, and an NPV of 87.5% for predicting pathological response.

Figure 3 ROC curves of the combined model to predict treatment response. ADC, apparent diffusion coefficient; AUC, area under the curve; CI, confidence interval; NLR, neutrophil-to-lymphocyte ratio; ROC, receiver operating characteristic.

Pathological response vs. non-response for survival outcomes

Survival curves of response and non-response are shown in Figure 4. The 4-year OS in the response group was 71.2% compared with 12.0% in the non-response group (P=0.002). The 4-year PFS in the response group was 72.7% compared with 10.0% in the non-response group (P<0.001).

Figure 4 Kaplan-Meier survival curves stratified by pathological response. (A) PFS; (B) OS. OS, overall survival; PFS, progression-free survival.

ADC, NLR, and the combined index for survival outcomes

In survival analysis, the combined index demonstrated a significant association with improved DFS (P=0.04), whereas neither ADC nor NLR alone showed a statistically significant effect (Figure 5A-5C). For OS, while no parameter reached statistical significance, the combined index exhibited a strong trend (P=0.12, Figure 5D-5F).

Figure 5 Kaplan-Meier survival curves stratified by ADC, NLR, and the combined model. (A-C) PFS; (D-F) OS. ADC, apparent diffusion coefficient; NLR, neutrophil-to-lymphocyte ratio; OS, overall survival; PFS, progression-free survival.

Discussion

In this study, we investigated the potential of baseline ADC values and NLR as independent predictors of treatment response to nCRT in patients with locally advanced ESCC. Both ADC and NLR at baseline demonstrated statistically significant differences between the responder and non-responder groups, although their discriminatory abilities ranged between 70–80% in sensitivity and specificity. Notably, we developed a logistic regression model that incorporated both ADC and NLR to predict responders, and this combined index exhibited enhanced prediction ability compared to their individual values. The model demonstrated a promising AUC of 0.840, signifying its potential as a valuable tool for predicting pathological response to nCRT in ESCC patients. Remarkably, the optimal cut-off value of 0.48 for the combined index offered robust sensitivity (95.0%), specificity (70.0%), PPV of 86.4%, and NPV of 87.5% for predicting pathological response in the model. These results highlight the potential clinical utility of the combined ADC and NLR index as a valuable biomarker in predicting the pathological response of patients with ESCC to nCRT. This integrated approach may aid clinicians in optimizing therapeutic strategies and facilitating personalized treatment decisions for these patients. Importantly, the model’s predictive capacity translates into clinically relevant outcomes, as evidenced by its significant correlation with PFS and a strong trend for OS.

To the best of our knowledge, this study represents the first attempt to integrate functional MRI data and host immunity markers in developing a prediction model for treatment response in ESCC patients. Traditionally, MRI scans have not been routinely employed as an examination modality for esophageal cancer patients. However, recent investigations have highlighted its promising value in clinical staging before surgery and as a predictor of treatment response. Our study findings indicated that lower baseline ADC values of the tumor were significantly associated with non-responders, aligning with prior research linking higher ADC values to improved survival outcomes in cancer patients (13,23-25). We attributed these observations to factors such as stromal collagen growth (24), which contributes to reduced blood flow and suboptimal therapeutic agent delivery due to increased interstitial fluid pressure (IFP) and osmotic pressure (24,26-29). Additionally, collagen-rich tumors tend to foster a hypoxic microenvironment, which may render them less responsive to radiotherapy. Wang et al. found that the ADC mean values of ESCC were negatively related to the expression level of p53 and Ki-67 (30). This may be explained by the fact that tumors with lower ADC values showed less response to nCRT. Identification of non-responders before nCRT enables us to guide individualized treatment decisions from the start avoiding the toxicity of chemoradiotherapy. In contrast to our results, one study found that higher pretreatment ADC values were associated with poor pathological response for esophageal cancer patients (31). They gave a hypothesis that higher pretreatment ADC values of tumor are characteristic of necrosis, poor fusion and hyperoxic environment, thus leading to resistance to nCRT. Another study concluded that there is no significant association between baseline ADC and treatment response (32). As for the NLR marker, previous evidence has suggested its significance in prognosis for ESCC patients (15-17). Elevated NLR prior to treatment has been linked to poor outcomes in patients who underwent surgery or concurrent chemoradiotherapy (18-21). In our study, we specifically explored the predictive value of pretreatment NLR for pathological response to neoadjuvant treatment. The results demonstrated NLR as an independent prognostic predictor of pathological response, with a sensitivity of 75% and specificity of 80% at an optimal threshold of 2.31. While some previous studies have reported conflicting results regarding the predictive ability of pretreatment NLR for pCR (33-36), our findings align with studies that identified NLR as a useful predictive marker. Our study’s predictive power was relatively higher, likely attributable to the inclusion of a more homogenous patient population who underwent nCRT, which resulted in a smaller number of cases. The predictive performance of ADC and NLR in esophageal cancer remains inconsistent across studies. Firstly, cohort heterogeneity, particularly the inclusion of both SCC and adenocarcinoma, affects results. Secondly, methodological differences in ADC measurement and NLR cut-off selection limit comparability. Furthermore, the inherent spatial-temporal heterogeneity of esophageal tumors contributes to biomarker volatility.

The potential of combined parameters in improving predictive value has been recognized, yet only a limited number of studies have explored the integration of functional imaging and host immunity markers for predicting pathological response in esophageal cancer patients. For instance, Wang et al. developed a predictive model utilizing standard uptake value (SUV) mean and NLR to assess treatment response to concurrent chemoradiotherapy in patients with ESCC. Their model demonstrated high accuracy in predicting patient prognosis, exhibiting improved specificity and PPV for treatment response (37). In contrast, Li et al. employed NLR and SUV as predictive factors for pCR in ESCC patients undergoing nCRT (38). Their study identified baseline NLR, NLR change, changes in SUV, and SUV change ratio as independent prognostic factors. Moreover, the combination of NLR change <3 and SUV change ratio >58% yielded the highest predictive value for PPV at 84.8%. In our study, we found that pretreatment NLR and ADC were both independent predictors of pathological response to nCRT in patients with ESCC. The combined NLR and ADC prediction model exhibited higher sensitivity (95.0% vs. 75.0% vs. 80.0%) and NPV (87.5% vs. 80.0% vs. 70.0%) compared to the individual parameters.

Nonetheless, our study has several limitations that warrant consideration. Firstly, due to its retrospective nature, the sample size was relatively small and the single-center study may potentially impact the generalisability of the findings. It is acknowledged that there are potential confounding factors that are not within the scope of control. In the future, a prospective study will be conducted. Secondly, performing MRI in the chest region remains challenging, as factors such as breathing and heartbeat movement can affect image quality. Further improvements in imaging techniques are required to address these challenges adequately. Lastly, the manual outlining of ROI in our study may introduce variations in ADC measurements, which should be acknowledged while interpreting the results.

Conclusions

In conclusion, the pre-treatment ADC-NLR index is a promising biomarker that may guide personalized nCRT strategies in ESCC by identifying patients for treatment modification. However, it is essential to acknowledge that further investigations with larger sample sizes are warranted to robustly validate and consolidate our findings.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://amj.amegroups.com/article/view/10.21037/amj-25-44/rc

Data Sharing Statement: Available at https://amj.amegroups.com/article/view/10.21037/amj-25-44/dss

Peer Review File: Available at https://amj.amegroups.com/article/view/10.21037/amj-25-44/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://amj.amegroups.com/article/view/10.21037/amj-25-44/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Zhengzhou University Affiliated Cancer Hospital (No. 2015ct068). The retrospective nature of the study led to the exemption of the requirement for informed consent from individual patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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