Imaging biomarkers for the treatment of esophageal cancer

Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature

BACKGROUND

The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed.

RESULTS

A total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767-0.900) for the training cohort and 0.733 (95% CI, 0.574-0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761-0.899) in the training cohort and 0.793 (95% CI, 0.653-0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795-0.928) in the training cohort and 0. 837 (95% CI, 0.705-0.969) in the test cohort.

CONCLUSION

An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC.

Predefined and data-driven CT radiomics predict recurrence-free and overall survival in patients with pulmonary metastases treated with stereotactic body radiotherapy

BACKGROUND

This retrospective study explores two radiomics methods combined with other clinical variables for predicting recurrence free survival (RFS) and overall survival (OS) in patients with pulmonary metastases treated with stereotactic body radiotherapy (SBRT).

METHODS

111 patients with 163 metastases treated with SBRT were included with a median follow-up time of 927 days. First-order radiomic features were extracted using two methods: 2D CT texture analysis (CTTA) using TexRAD software, and a data-driven technique: functional principal components analysis (FPCA) using segmented tumoral and peri-tumoural 3D regions.

RESULTS

Using both Kaplan-Meier analysis with its log-rank tests and multivariate Cox regression analysis, the best radiomic features of both methods were selected: CTTA-based "entropy" and the FPCA-based first mode of variation of tumoural CT density histogram: "F1." Predictive models combining radiomic variables and age showed a C-index of 0.62 95% with a CI of (0.57-0.67). "Clinical indication for SBRT" and "lung primary cancer origin" were strongly associated with RFS and improved the RFS C-index: 0.67 (0.62-0.72) when combined with the best radiomic features. The best multivariate Cox model for predicting OS combined CTTA-based features-skewness and kurtosis-with size and "lung primary cancer origin" with a C-index of 0.67 (0.61-0.74).

CONCLUSION

In conclusion, concise predictive models including CT density-radiomics of metastases, age, clinical indication, and lung primary cancer origin can help identify those patients with probable earlier recurrence or death prior to SBRT treatment so that more aggressive treatment can be applied.

Spectral CT-based nomogram for evaluation of neoadjuvant chemotherapy response in esophageal squamous cell carcinoma

OBJECTIVES

To establish a spectral CT-based nomogram for predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study included 172 patients with ESCC who underwent spectral CT scans before NAC followed by resection. Based on postoperative tumor regression grades (TRG), 34% (58) of patients were responsive (TRG1) and 66% (114) were non-responsive (TRG2-3). The data was divided into a primary set of 120 and a validation set of 52, maintaining a 7:3 random ratio. Measurements included iodine concentration (IC), normalized iodine concentration (nIC), CT40kev, CT70kev, spectral attenuation curve slope (λHU), and effective atomic number (Zeff) during non-contrast and venous phases (VP). Clinicopathologic characteristics were collected. Univariable and multivariable logistic regressions identified independent predictors of NAC response. The model was visualized using nomograms, and its efficacy was assessed via receiver operating characteristic (ROC) curves.

RESULTS

Multivariable logistic regression analysis identified the neutrophil-to-lymphocyte ratio (NLR), clinical stage, ZeffVP, and nICVP as independent predictors of NAC response. The nomogram incorporating all four independent predictors, outperformed spectral CT and the clinical model with the highest AUCs of 0.825 (95% CI: 0.746-0.895) for the primary set and 0.794 (95% CI: 0.635-0.918) for the validation set (DeLong test: all p < 0.05).

CONCLUSIONS

The spectral CT and clinical models were useful in predicting NAC response in ESCC patients. Combining spectral CT imaging parameters and clinicopathologic characteristics in a nomogram improved predictive accuracy.

KEY POINTS

Question Developing a non-invasive, practical tool to predict ESCC's response to chemotherapy is crucial and has not yet been done. Findings This nomogram, incorporating clinicopathologic characteristics and spectral CT-derived parameters, predicted NAC response in ESCC patients. Clinical relevance This spectral CT-based nomogram is a non-invasive and easily obtainable tool for accurately predicting ESCC response to NAC, aiding clinicians in personalized treatment planning.

Novel Advances in Qualitative Diagnostic Imaging for Decision Making in Multidisciplinary Treatment for Advanced Esophageal Cancer

Background: Recently, neoadjuvant therapy and the succeeding surgery for advanced esophageal cancer have been evaluated. In particular, the response to the therapy has been found to affect surgical outcomes, and thus a precise evaluation of treatment effect is important for this strategy. In this study, articles on qualitative diagnostic modalities to evaluate tumor activities were reviewed, and the diagnostic indices were examined. Methods: For prediction of the effect, perfusion CT and diffusion MRI were estimated. For the histological response evaluation, perfusion CT, diffusion-MRI, and FDG-PET were estimated. For downstaging evaluation of T4, tissue-selective image reconstruction using enhanced CT was estimated and diagnostic indices were reviewed. Results: The prediction of the effect using perfusion CT with 'pre CRT blood flow' and diffusion MRI with 'pre CRT ADC value'; the estimation of the histological response using perfusion CT with 'post CRT blood flow reduction, using diffusion MRI with 'post CRT ADC increasing', and using FDG-PET with 'post CRT SUV reduction'; and the downstaging evaluation of T4 using CT image reconstruction with 'fibrous changed layer' were performed well, respectively. Conclusions: Qualitative imaging modalities for prediction or response evaluation of neoadjuvant therapy for progressive esophageal cancer were useful for the decision making of the treatment strategy of the multidisciplinary treatment.

An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study

Radiotherapy benefits patients with advanced esophageal squamous cell carcinoma (ESCC) in terms of symptom relief and long-term survival. In contrast, a substantial proportion of ESCC patients have not benefited from radiotherapy. This study aimed to establish and validate an artificial neural network-based radiomics model for the pretreatment prediction of the radiotherapy response of advanced ESCC by using integrated data combined with feasible baseline characteristics of computed tomography. A total of 248 patients with advanced ESCC who underwent baseline CT and received radiotherapy were enrolled in this study and were analyzed by two types of radiomics models, machine learning and deep learning. As a result, the Att. Resnet50 pretrained network model indicated superior performance, with AUCs of 0.876, 0.802 and 0.732 in the training, internal validation, and external validation cohorts, respectively. Similarly, our Att. Resnet50 pretrained network model showed excellent calibration and significant clinical benefit according to the C index and decision curve analysis. Herein, a novel pretreatment radiomics model was established based on deep learning methods and could be used for radiotherapy response prediction in advanced ESCC patients, thus providing reliable evidence for therapeutic decision-making.

Biomarkers for Early Detection, Prognosis, and Therapeutics of Esophageal Cancers

Esophageal cancer (EC) is the deadliest cancer worldwide, with a 92% annual mortality rate per incidence. Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are the two major types of ECs, with EAC having one of the worst prognoses in oncology. Limited screening techniques and a lack of molecular analysis of diseased tissues have led to late-stage presentation and very low survival durations. The five-year survival rate of EC is less than 20%. Thus, early diagnosis of EC may prolong survival and improve clinical outcomes. Cellular and molecular biomarkers are used for diagnosis. At present, esophageal biopsy during upper endoscopy and histopathological analysis is the standard screening modality for both ESCC and EAC. However, this is an invasive method that fails to yield a molecular profile of the diseased compartment. To decrease the invasiveness of the procedures for diagnosis, researchers are proposing non-invasive biomarkers for early diagnosis and point-of-care screening options. Liquid biopsy involves the collection of body fluids (blood, urine, and saliva) non-invasively or with minimal invasiveness. In this review, we have critically discussed various biomarkers and specimen retrieval techniques for ESCC and EAC.

Potential use of high-resolution T2-weighted MRI with histopathologic findings in staging esophageal cancer

BACKGROUND

Magnetic resonance imaging (MRI) has shown promising capabilities in diagnosing local esophageal carcinoma. This study investigated the clinical value of high resolution (HR; small field of view and continuous thin section) axial T2-weighted MRI (HR-T2WI) as a noninvasive method for esophageal carcinoma tumor staging (T staging).

METHODS

Forty-two patients with biopsy-proven esophageal cancer were investigated using HR-T2WI. The discrepancies between the esophageal wall layers and tumor tissue were assessed for MRI T staging using a visual MRI signal intensity scale (low, intermediate, and high intensities). The computed tomography (CT) and MRI T staging was compared with whole-mount histopathological sections in all patients who underwent resection.

RESULTS

HR-T2WI provided a thorough view of the esophageal wall and the tumor's anatomic layers. Of the 42 patients with histological tumors (HTs), there were 6 cases with tumors classified as HT-1a, 5 cases with HT-1b, 14 cases with HT-2, and 17 cases with HT-3/4, and their MRI T stages were 5 MRI-T1a, 6 MRI-T1b, 14 MRI-T2, and 17 MRI-T3/4, respectively. After analyzing the imaging presentation at different HT staginess, we found that HR-T2WI enabled a more accurate classification than was possible with CT. The difference in accuracy between CT and T2WI was statistically significant (P<0.05) in the entire sample and in HT1-2 tumors and HT3-4 tumors.

CONCLUSIONS

HR-T2WI clearly identified normal esophageal wall layers; it had high diagnostic accuracy when evaluating tumor invasion and in MRI-T staging for esophageal carcinoma. This study established staging criteria of esophageal carcinoma using HR-T2WI and indicated that this approach could be used as a supplemental noninvasive method for the local T staging of esophageal carcinoma.

HOXA10 enhances cell proliferation and suppresses apoptosis in esophageal cancer via activating p38/ERK signaling pathway

Esophageal cancer (EC) is an extremely aggressive malignant tumor. Homeobox A10 (HOXA10) is highly expressed and plays an important role in a variety of tumors. However, the function of HOXA10 in EC remains unclear. In this study, HOXA10 was observed to highly express in EC tissues and cells. Interestingly, the CCK-8 assay, flow cytometry, and colony formation assay confirmed that overexpression of HOXA10 promoted proliferation and suppressed cell apoptosis in EC cells. More importantly, the western blot assay indicated that the phosphorylation levels of ERK and p38 were elevated in EC cells overexpressed HOXA10, indicating that overexpression of HOXA10 activated p38/ERK signaling pathway in EC cells. These findings concluded that HOXA10 aggravated EC progression via activating p38/ERK signaling pathway, providing a potential therapeutic target for EC.

LncRNA SNHG3 promoted cell proliferation, migration, and metastasis of esophageal squamous cell carcinoma via regulating miR-151a-3p/PFN2 axis

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignant tumor with a poor prognosis. The dysregulation of long non-coding RNAs (lncRNAs) is closely related to the tumorigenesis and progression of ESCC. However, the effects of lncRNA small nucleolar RNA host gene 3 (lncRNA SNHG3) in ESCC are still unclear. Therefore, a series of experiments methods, such as quantitative real-time polymerase chain reaction, function gain/loss experiments, western blots, and animal xenograft tumor model, were employed to explore the biological function and molecular mechanism of SNHG3 in ESCC. As results, we first reported that SNHG3 was significantly up-regulated in ESCC tissues and cells. SNHG3 knockdown obviously inhibited cell proliferation, migration, invasion, and promoted apoptosis. Mechanism analysis revealed that SNHG3 sponged miR-151a-3p to regulate PFN2. Inhibition of miR-151a-3p and overexpression of PFN2 attenuated the positive effect of SNHG3 knockdown on suppressing tumor progression. Furthermore, the anti-tumor effects of SNHG3 knockdown were also observed in vivo. In summary, our results indicated that SNHG3 knockdown suppressed tumor development via the miR-151a-3p/PFN2 axis, and targeting SNHG3 may provide a new opportunity for ESCC patients.

Development of imaging biomarker for esophageal cancer using intravoxel incoherent motion MRI

BACKGROUND

Intravoxel incoherent motion MRI (IVIM-MRI) can quantify micro-perfusion at the capillary level in the tissue. The purpose of this study is to measure tumor perfusion using IVIM-MRI, and evaluate its value as a biomarker to predict prognosis in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

109 ESCC patients (93 men and 16 women; median age: 72) who underwent IVIM-MRI prior to treatment between February 2018 and August 2020 were retrospectively investigated. Both mean apparent diffusion coefficient (ADC) value and mean perfusion-related parameter (PP) value of the primary tumor were measured using three b values of 0, 400, and 1000 s/mm2 based on the IVIM model. We analyzed associations of these parameters with clinical stage and disease-specific survival (DSS).

RESULTS

Lower ADC and PP values of the tumor were significantly associated with the higher clinical T stage (p < 0.0001, p < 0.0001, respectively). In Kaplan-Meier analyses, patients with lower PP value tumors (< 18.94, median) had significantly worse DSS (p < 0.0001), while tumor ADC value did not show a significant correlation with DSS. In a multivariate analysis, PP value of the tumor was an independent prognostic factor for DSS (p = 0.0027).

CONCLUSIONS

Quantification of tumor perfusion using IVIM-MRI can be a non-invasive prognostic biomarker of ESCC, reflecting clinical stage and survival.

Computed tomography-derived biomarker for predicting the treatment response to neoadjuvant chemoradiotherapy of rectal cancer

BACKGROUND

Malignant tumor essentially implies structural heterogeneity. Analysis of medical imaging can quantify this structural heterogeneity, which can be a new biomarker. This study aimed to evaluate the usefulness of texture analysis of computed tomography (CT) imaging as a biomarker for predicting the therapeutic response of neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer.

METHODS

We enrolled 76 patients with rectal cancer who underwent curative surgery after nCRT. Texture analyses (Fractal analysis and Histogram analysis) were applied to contrast-enhanced CT images, and fractal dimension (FD), skewness, and kurtosis of the tumor were calculated. These CT-derived parameters were compared with the therapeutic response and prognosis.

RESULTS

Forty-six of 76 patients were diagnosed as clinical responders after nCRT. Kurtosis was significantly higher in the responders group than in the non-responders group (4.17 ± 4.16 vs. 2.62 ± 3.19, p = 0.04). Nine of 76 patients were diagnosed with pathological complete response (pCR) after surgery. FD of the pCR group was significantly lower than that of the non-pCR group (0.90 ± 0.12 vs. 1.01 ± 0.12, p = 0.009). The area under the receiver-operating characteristics curve of tumor FD for predicting pCR was 0.77, and the optimal cut-off value was 0.84 (accuracy; 93.4%). Furthermore, patients with lower FD tumors tended to show better relapse-free survival and disease-specific survival than those with higher FD tumors (5-year, 80.8 vs. 66.6%, 94.4 vs. 80.2%, respectively), although it was not statistically significant (p = 0.14, 0.11).

CONCLUSIONS

CT-derived texture parameters could be potential biomarkers for predicting the therapeutic response of rectal cancer.

Long noncoding RNA KCNMB2-AS1 promotes the development of esophageal cancer by modulating the miR-3194-3p/PYGL axis

Esophageal cancer (ESCA), as a common cancer worldwide, is a main cause of cancer-related mortality. Comprehensive studies on molecular mechanism of ESCA have been carried out. Though numerous long noncoding RNAs (lncRNAs) was reported to participate in the occurrence and development of ESCA, the potential role of lncRNA potassium calcium-activated channel subfamily M regulatory beta subunit 2 (KCNMB2) antisense RNA 1 (KCNMB2-AS1) in ESCA remains to be discovered. This study intends to investigate the detailed function and molecular mechanism of KCNMB2-AS1 in ESCA. Gene expression was evaluated by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Cell proliferation was examined by Cell Counting Kit-8 (CCK-8) assay and colony formation assay. Cell invasion and migration were measured by wound healing assay and Transwell assay. Luciferase reporter assay was adopted to validate the interaction between KCNMB2-AS1 and miR-3194-3p. Western blotting was performed to assess protein levels. We discovered that KCNMB2-AS1 was significantly upregulated in ESCA. KCNMB2-AS1 downregulation suppressed the growth, invasion, migration and stemness of ESCA cells. KCNMB2-AS1 bound with miR-3194-3p, and glycogen phosphorylase L (PYGL) was a direct target of miR-3194-3p. KCNMB2-AS1 upregulated PYGL expression by directly binding with miR-3194-3p. Additionally, PYGL overexpression abolished the inhibitory influence of KCNMB2-AS1 depletion on ESCA cell behaviors. Collectively, lncRNA KCNMB2-AS1 promotes ESCA development through targeting the miR-3194-3p/ PYGL axis, which might provide theoretical basis to explore novel biomarkers for ESCA treatment.

Human Gut Microbiota-associated Gastrointestinal Malignancies: A Comprehensive Review

The human gastrointestinal tract houses trillions of microbes. The gut and various types of microorganisms, including bacteria, viruses, fungi, and archaea, form a complex ecosystem known as the gut microbiota, and the whole genome of the gut microbiota is referred to as the gut microbiome. The gut microbiota is essential for homeostasis and the overall well-being of a person and is increasingly considered an adjunct "virtual organ," with a complexity level comparable to that of the other organ systems. The gut microbiota plays an essential role in nutrition, local mucosal homeostasis, inflammation, and the mucosal immune system. An imbalanced state of the gut microbiota, known as dysbiosis, can predispose to development of various gastrointestinal malignancies through three speculated pathogenic mechanisms: (a) direct cytotoxic effects with damage to the host DNA, (b) disproportionate proinflammatory signaling inducing inflammation, and (c) activation of tumorigenic pathways or suppression of tumor-suppressing pathways. Several microorganisms, including Helicobacter pylori, Epstein-Barr virus, human papillomavirus, Mycoplasma species, Escherichia coli, and Streptococcus bovis, are associated with gastrointestinal malignancies such as esophageal adenocarcinoma, gastric adenocarcinoma, gastric mucosa-associated lymphoid tissue lymphoma, colorectal adenocarcinoma, and anal squamous cell carcinoma. Imaging plays a pivotal role in diagnosis and management of microbiota-associated gastrointestinal malignancies. Appropriate use of probiotics, fecal microbiota transplantation, and overall promotion of the healthy gut are ongoing areas of research for prevention and treatment of malignancies. Online supplemental material is available for this article. ^©RSNA, 2021.

Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies

The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on non-invasive, radiological imaging to diagnose, stage, and treat these malignancies. Oesophageal, gastric, and colorectal malignancies require specialist investigation and treatment due to the complex nature of the anatomy, biology, and subsequent treatment strategies. As cancer imaging techniques develop, many opportunities to improve tumour detection, diagnostic accuracy and treatment monitoring present themselves. This review article aims to report current imaging practice, advances in various radiological modalities in relation to GI luminal tumour sites and describes opportunities for GI radiologists to improve patient outcomes.

Advanced endoscopic imaging for detecting and guiding therapy of early neoplasias of the esophagus

Esophageal cancers, largely adenocarcinoma in Western countries and squamous cell cancer in Asia, present a significant burden of disease and remain one of the most lethal of cancers. Key to improving survival is the development and adoption of new imaging modalities to identify early neoplastic lesions, which may be small, multifocal, subsurface, and difficult to detect by standard endoscopy. Such advanced imaging is particularly relevant with the emergence of ablative techniques that often require multiple endoscopic sessions and may be complicated by bleeding, pain, strictures, and recurrences. Assessing the specific location, depth of involvement, and features correlated with neoplastic progression or incomplete treatment may optimize treatments. While not comprehensive of all endoscopic imaging modalities, we review here some of the recent advances in endoscopic luminal imaging, particularly with surface contrast enhancement using virtual chromoendoscopy, highly magnified subsurface imaging with confocal endomicroscopy, optical coherence tomography, elastic scattering spectroscopy, angle-resolved low-coherence interferometry, and light scattering spectroscopy. While there is no single ideal imaging modality, various multimodal instruments are also being investigated. The future of combining computer-aided assessments, molecular markers, and improved imaging technologies to help localize and ablate early neoplastic lesions shed hope for improved disease outcome.

Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review

Radiomics or textural feature extraction obtained from positron emission tomography (PET) images through complex mathematical models of the spatial relationship between multiple image voxels is currently emerging as a new tool for assessing intra-tumoral heterogeneity in medical imaging. In this paper, available literature on texture analysis using FDG PET imaging in patients suffering from tumors of the gastro-intestinal tract is reviewed. While texture analysis of FDG PET images appears clinically promising, due to the lack of technical specifications, a large variability in the implemented methodology used for texture analysis and lack of statistical robustness, at present, no firm conclusions can be drawn regarding the predictive or prognostic value of FDG PET texture analysis derived indices in patients suffering from gastro-enterologic tumors. In order to move forward in this field, a harmonized image acquisition and processing protocol as well as a harmonized protocol for texture analysis of tumor volumes, allowing multi-center studies excluding statistical biases should be considered. Furthermore, the complementary and additional value of CT-imaging, as part of the PET/CT imaging technique, warrants exploration.

Volumetric Histogram Analysis of Apparent Diffusion Coefficient as a Biomarker to Predict Survival of Esophageal Cancer Patients

BACKGROUND

The purpose of this study was to investigate whether histogram analysis of an apparent diffusion coefficient (ADC) can serve as a prognostic biomarker for esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study enrolled 116 patients with ESCC who received curative surgery from 2006 to 2015 (including 70 patients who received neoadjuvant chemotherapy). Diffusion-weighted magnetic resonance imaging (DWI) was performed prior to treatment. The ADC maps were generated by DWIs at b = 0 and 1000 (s/mm2), and analyzed to obtain ADC histogram-derived parameters (mean ADC, kurtosis, and skewness) of the primary tumor. Associations of these parameters with pathological features were analyzed, and Cox regression and Kaplan-Meier analyses were performed to compare these parameters with recurrence-free survival (RFS) and disease-specific survival (DSS).

RESULTS

Kurtosis was significantly higher in tumors with lymphatic invasion (p = 0.005) with respect to the associations with pathological features. In univariate Cox regression analysis, tumor depth, lymph node status, mean ADC, and kurtosis were significantly correlated with RFS (p = 0.047, p < 0.001, p = 0.037, and p < 0.001, respectively), while lymph node status and kurtosis were also correlated with DSS (p = 0.002 and p = 0.017, respectively). Furthermore, multivariate analysis demonstrated that kurtosis was the independent prognostic factor for both RFS and DSS (p < 0.001 and p = 0.015, respectively). In Kaplan-Meier analysis, patients with higher kurtosis tumors (> 3.24) showed a significantly worse RFS and DFS (p < 0.001 and p = 0.006, respectively).

CONCLUSIONS

Histogram analysis of ADC may serve as a useful biomarker for ESCC, reflecting pathological features and prognosis.

Volumetric histogram analysis of apparent diffusion coefficient for predicting pathological complete response and survival in esophageal cancer patients treated with chemoradiotherapy

BACKGROUND

The purpose of the study was to evaluate whether histogram analysis of apparent diffusion coefficient (ADC) can predict pathological complete response (pCR) and survival in patients with esophageal squamous cell carcinoma (ESCC) after chemoradiotherapy (CRT).

METHODS

We retrospectively identified 58 patients with ESCC who underwent surgery after CRT between 2007 and 2016. Associations of pretreatment histogram derived ADC parameters with pathological response and survival were analyzed.

RESULTS

Tumors achieved pCR (10 patients, 17.2%) showed significant lower ADC, higher kurtosis, and higher skewness than those of non-pCR (p = 0.005, 0.007, <0.001, respectively). Receiver operating characteristics analysis demonstrated skewness was the best predictor for pCR (AUC = 0.86), with a cut off value of 0.50 (accuracy, 86.2%). In Kaplan-Meier analysis, patients with higher skewness tumors (≥0.50) showed a significantly better recurrence free survival (p = 0.032, log-rank).

CONCLUSIONS

Histogram analysis of ADC can enable prediction of pCR and survival in ESCC patients treated with preoperative CRT.

A SHORT SUMMARY

ADC histogram analysis can be an imaging biomarker for esophageal cancer patients treated with CRT.