Clinical significance of mucinous rectal adenocarcinoma following preoperative chemoradiotherapy and curative surgery

The Clinical Value of Apparent Diffusion Coefficient of Readout Segmentation of Long Variable Echo Trains and Correlation With Ki-67 Expression in Distal Rectal Cancer

OBJECTIVE

The aim of the study is to explore the clinical value of the apparent diffusion coefficient (ADC) derived from the readout segmentation of long variable echo trains (RESOLVE) technique for identifying clinicopathologic features of distal rectal cancer and correlations between ADC and Ki-67 expression.

METHODS

The data of 112 patients with a proven pathology of distal rectal cancer who underwent preoperative magnetic resonance imaging were retrospectively analyzed. The mean ADC value was measured using the "full-layer and center" method. Differences in ADC values and Ki-67 expression in different clinical stages, pathological types, and tumor differentiation were compared using analysis of variance. Correlations between ADC value and clinicopathologic features were assessed using Spearman correlation analysis.

RESULTS

Interobserver agreement of confidence levels from 2 radiologists was excellent for ADC measurement ( k =  0.85). Patients with a lower clinical stage, well-differentiated adenocarcinomas, and a higher possibility of mucinous adenocarcinoma exhibited a positive correlation with higher ADC values, but these factors were negatively correlated with Ki-67 expression (all P < 0.05). We found that ADC value was negatively correlated with Ki-67 expression ( r = -0.62, P < 0.001).

CONCLUSIONS

The ADC value generated by RESOLVE sequences was significantly associated with clinicopathologic features and Ki-67 expression in patients with distal rectal cancer in this study. Thus, the ADC value could be considered a new noninvasive imaging biomarker that could be helpful in predicting the biological properties of distal rectal cancer.

Application of liver acquisition with volume acceleration enhanced sequence in improving the accuracy of reassessing organ-invasive rectal mucinous adenocarcinoma after chemoradiation

OBJECTIVES

To explore the ability of liver acquisition with volume acceleration contrast-enhanced sequence (LAVA-ce) to improve the accuracy of reassessing adjacent organ involvement by rectal mucinous adenocarcinoma (MC) after neoadjuvant therapy (NAT).

METHODS

This study retrospectively enrolled twenty-five patients with MC who underwent pre- and post-NAT MRI, were staged as T4b using pre-NAT T₂ weighted imaging, received NAT and underwent radical resection. All MR images were divided into two schemes, T₂ weighted plus diffusion weighted imaging (T₂D_w protocol) and plus LAVA-ce (T₂D_wL_ce protocol). All patients were scored on a 0-4 scale to reassess organ-invasive mucus components. Postoperative pathology was used to identify the involvement of surrounding organs (ypT4b). The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the consistency of the results with pathology after adding fs-CE sequence.

RESULTS

Among 25 MC patients (15 males and 10 females, aged 21-89 years), 21 were restaged as yT4b after NAT by using T₂D_w, with an accuracy of 44.0 % (11/25), which was lower than the accuracy of staging patients with non-mucinous rectal adenocarcinoma (94.1 %, 96/102). The accuracy of MC restaging was improved by using T₂D_wL_ce (23/25). The AUC of T₂D_wL_ce was 0.857 (95 % CI, 0.660∼0.964), which was higher than that of T₂D_w (AUC, 0.611 [95 % CI, 0.397∼0.798]) (P = 0.019).

CONCLUSION

The LAVA-ce sequence can improve the accuracy of reevaluation and should be included in the MRI protocol for MC patients.

An inflammation index-based prediction of treatment response to neoadjuvant chemoradiotherapy for rectal mucinous adenocarcinoma

OBJECTIVE

This study aimed to evaluate the predictive value of hematological inflammation-based indexes in the treatment response to neoadjuvant chemoradiotherapy (NCRT) in rectal mucinous adenocarcinomas (MACs).

METHODS

Patients with rectal MACs undergoing NCRT and curative resection were included. Inflammation-based indexes such as systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI) were calculated. Receiver operator characteristics analysis was used to determine the optimal cutoff points. Multivariable logistic analysis identified predictors of good response to NCRT. A nomogram was developed and validated internally.

RESULTS

A total of 100 patients met the inclusion criteria, with 32 patients developing good response (tumor regression grade, TRG 0 + 1) to NCRT. Lower pre-treatment SII, NLR, and PLR levels were associated with a higher probability of good response to NCRT (P = 0.025, P < 0.001, P = 0.003, respectively), and a higher pre-treatment PNI level was associated with a higher probability of good response to NCRT (P = 0.005). Logistic regression analysis demonstrated that tumor size (< 3 cm, OR = 5.489, P = 0.025), pre-treatment NLR level (< 3.05, OR = 4.025, P = 0.028), pre-treatment PLR level (< 145.98, OR = 4.337, P = 0.038), and pre-treatment PNI level (≥ 41.32, OR = 3.477, P = 0.039) were independent predictors of good response to NCRT. A nomogram was developed with a C-index of 0.827.

CONCLUSION

Hematological inflammation-based indexes, in terms of pre-treatment NLR, PLR, and PNI levels, can help in predicting the treatment response to NCRT for rectal MACs.

Mucinous rectal cancer: concepts and imaging challenges

Rectal adenocarcinoma with mucinous components is an uncommon type of rectal cancer with two distinct histologic subtypes: mucinous adenocarcinoma and signet-ring cell carcinoma. Mucin can also be identified as pattern of response after neoadjuvant treatment. On imaging modalities, mucin typically demonstrates high signal intensity on T2-weighted images, low attenuation on computed tomography, and may be negative on 18-fluorodeoxyglucose positron emission tomography. After neoadjuvant CRT, cellular and acellular mucin share similar imaging features, and differentiating them is currently the main challenge faced by radiologists. Radiologists should be aware of pros, cons, and limitations of each imaging modality in the primary staging and restaging to avoid misinterpretation of the radiological findings.

Mucinous rectal cancer: concepts and imaging challenges

Rectal adenocarcinoma with mucinous components is an uncommon type of rectal cancer with two distinct histologic subtypes: mucinous adenocarcinoma and signet-ring cell carcinoma. Mucin can also be identified as pattern of response after neoadjuvant treatment. On imaging modalities, mucin typically demonstrates high signal intensity on T2-weighted images, low attenuation on computed tomography, and may be negative on 18-fluorodeoxyglucose positron emission tomography. After neoadjuvant CRT, cellular and acellular mucin share similar imaging features, and differentiating them is currently the main challenge faced by radiologists. Radiologists should be aware of pros, cons, and limitations of each imaging modality in the primary staging and restaging to avoid misinterpretation of the radiological findings.

Imaging findings for mucinous tumors of the abdomen and pelvis

This article aims to describe the imaging findings for mucinous tumors of the abdomen and pelvis, which have a similar appearance on imaging tests regardless of the organ in which they develop. Due to the high water content of mucus, the appearance of these tumors is generally similar to that of water on ultrasonography, computed tomography, and magnetic resonance imaging. Another common feature of mucin-producing tumors is that calcifications are often present. The rupture of these lesions and accumulation of mucinous material in the peritoneal cavity gives rise to pseudomyxoma peritonei. It is important to identify mucinous tumors because they have a different prognosis and clinical course than non-mucinous tumors and require different management. Depending on their anatomic location and their imaging characteristics, the treatment approach varies from follow-up to radical surgery together with chemotherapy or radiotherapy or both.

Are ADC values of readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) correlated with pathological prognostic factors in rectal adenocarcinoma?

Background

Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values as imaging biomarkers of rectal cancer are currently a hot research spot. The use of ADC values for preoperative judgment of pathological features in rectal cancer has been generally accepted. The image quality evaluation of conventional diffusion is severe deformation, and the measurement of ADC values can easily lead to bias. Readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) provides high signal-to-noise ratio images and significantly reduces distortions caused by magnetosensitive effects. The purpose of this study was to explore the correlations between ADC values of RESOLVE and pathological prognostic factors in rectal adenocarcinoma.

Methods

We collected pathological data of 89 patients with pathologically confirmed rectal adenocarcinoma who directly underwent surgical resection without receiving adjuvant therapy. The patients were grouped according to the pathologic type, gross classification, degree of differentiation, TN stage, and immunohistochemical expression of epidermal growth factor receptor (EGFR).

Results

RESOLVE ADC values of rectal cancer were measured at b = 800, and correlations between the RESOLVE ADC values obtained in different groups were analysed. We found that RESOLVE ADC values in the ulcer-type group were significantly higher than those in the eminence-type group.

Conclusion

RESOLVE ADC values in different pathologic types of rectal cancer were significantly different. RESOLVE ADC values in the EGFR-positive group were significantly lower than those in the EGFR-negative group. There was no significant difference in RESOLVE ADC values between different degrees of pathologic differentiation, TN stages, and positive or negative lymph nodes. The quantitative description of RESOLVE ADC values could be used to assess the biological behaviour of rectal adenocarcinoma.

Prognostic influence of histopathological regression patterns in rectal adenocarcinoma receiving neoadjuvant therapy

BACKGROUND

Neoadjuvant chemoradiation therapy (CRT) is an important management strategy in rectal carcinoma. Different systems grading response have shown varying prognostic influence.

METHODS

To analyze the prognostic influence of pathological response in a series of 183 patients with rectal carcinoma receiving neoadjuvant therapy. To determine the prognostic significance of the histopathological patterns of response.

RESULTS

A total of 183 patients from two hospitals. The concordance rate between pathologists was good. In total, 18% of the patients showed grade 0 (complete response), 31.7% grade 1, 19.2% grade 2 and 31.1% grade 3 regression. T down-staging was found in 51.9% of the cases. 46 patients recurred and 18 died of disease (median follow-up time: 39 months). We found a statistically significant association between pathological response and pT stage and down-staging. Inflammatory reaction in the tumor bed was significantly associated to regression and prognosis. Cox's multivariate analysis of survival revealed that down-staging and presence of mucin pools in the tumor bed behaved as significant predictors of recurrence and regression grade and mucin pools as significant predictors of survival.

CONCLUSIONS

Pathological response is an important surrogate marker of prognosis in some large series, but results are varying. There are many systems to grade regression and this makes it difficult to compare the results by different groups. It is important to report the specific pattern of response, for some of them may have prognostic relevance. We feel there is an urgent need to develop standarized protocols and employ a universal regression scheme if we intend to use this factor to guide therapy.