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

PARP1-targeted fluorescence molecular endoscopy as novel tool for early detection of esophageal dysplasia and adenocarcinoma

BACKGROUND

Esophageal cancer is one of the 10 most common cancers worldwide and its incidence is dramatically increasing. Despite some improvements, the current surveillance protocol with white light endoscopy and random untargeted biopsies collection (Seattle protocol) fails to diagnose dysplastic and cancerous lesions in up to 50% of patients. Therefore, new endoscopic imaging technologies in combination with tumor-specific molecular probes are needed to improve early detection. Herein, we investigated the use of the fluorescent Poly (ADP-ribose) Polymerase 1 (PARP1)-inhibitor PARPi-FL for early detection of dysplastic lesions in patient-derived organoids and transgenic mouse models, which closely mimic the transformation from non-malignant Barrett's Esophagus (BE) to invasive esophageal adenocarcinoma (EAC).

METHODS

We determined PARP1 expression via immunohistochemistry (IHC) in human biospecimens and mouse tissues. We also assessed PARPi-FL uptake in patient- and mouse-derived organoids. Following intravenous injection of 75 nmol PARPi-FL/mouse in L2-IL1B (n = 4) and L2-IL1B/IL8Tg mice (n = 12), we conducted fluorescence molecular endoscopy (FME) and/or imaged whole excised stomachs to assess PARPi-FL accumulation in dysplastic lesions. L2-IL1B/IL8Tg mice (n = 3) and wild-type (WT) mice (n = 2) without PARPi-FL injection served as controls. The imaging results were validated by confocal microscopy and IHC of excised tissues.

RESULTS

IHC on patient and murine tissue revealed similar patterns of increasing PARP1 expression in presence of dysplasia and cancer. In human and murine organoids, PARPi-FL localized to PARP1-expressing epithelial cell nuclei after 10 min of incubation. Injection of PARPi-FL in transgenic mouse models of BE resulted in the successful detection of lesions via FME, with a mean target-to-background ratio > 2 independently from the disease stage. The localization of PARPi-FL in the lesions was confirmed by imaging of the excised stomachs and confocal microscopy. Without PARPi-FL injection, identification of lesions via FME in transgenic mice was not possible.

CONCLUSION

PARPi-FL imaging is a promising approach for clinically needed improved detection of dysplastic and malignant EAC lesions in patients with BE. Since PARPi-FL is currently evaluated in a phase 2 clinical trial for oral cancer detection after topical application, clinical translation for early detection of dysplasia and EAC in BE patients via FME screening appears feasible.

Exploratory Study of a New Technique of Pixelated Chromoendoscopy in the Diagnosis of Early Esophageal Cancer

BACKGROUND

Chromoendoscopy is an effective method for early screening of esophageal cancer, but diagnosis can depend on subjective judgment. The study aimed to explore a new technique of pixelated chromoendoscopy in the diagnosis of early esophageal cancer.

PATIENTS AND METHODS

The study included patients with symptoms of esophageal cancer who attended Jiangyin People's Hospital between January 2015 and July 2021. Chromoendoscopy was performed on each patient. The images then underwent digital analysis; the lesion area (the sensitive region) was pixelated by dividing it into the smallest image unit and the red, green, and blue color components. The diagnostic performance of pixelated chromoendoscopy was evaluated by calculating the area under the receiver operating characteristic.

RESULTS

The study finally enrolled 86 patients (aged 51.34 ± 5.82 y), including 54 males and 32 females. Pathologic diagnosis identified 54 cases in the cancer group and 32 cases in the non-cancer group. Traditional judgment had a diagnostic sensitivity of 70.73% and specificity was 75.00%. Pixelated chromoendoscopy sensitivity was 80.49%, and specificity was 83.33%. The area under the receiver operating characteristic was 0.814, at a cutoff value of 0.625, indicating a good prediction effect.

CONCLUSIONS

These results showed that pixelated chromoendoscopy might improve the rate of esophageal cancer diagnoses from early screening.

Quantitative multiphoton imaging of cell metabolism, stromal fibers, and keratinization enables label-free discrimination of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) features atypical clinical manifestations and a low 5-year survival rate (< 5% in many developing countries where most of the disease occurs). Precise ESCC detection and grading toward timely and effective intervention are therefore crucial. In this study, we propose a multidimensional, slicing-free, and label-free histopathological evaluation method based on multispectral multiphoton fluorescence lifetime imaging microscopy (MM-FLIM) for precise ESCC identification. To assess the feasibility of this method, comparative imaging on fresh human biopsy specimens of different ESCC grades is performed. By constructing fluorescence spectrum- and lifetime-coded images, ESCC-induced morphological variations are unveiled. Further quantification of cell metabolism and stromal fibers reveals potential indicators for ESCC detection and grading. The specific identification of keratin pearls provides additional support for the early detection of ESCC. These findings demonstrate the viability of using MM-FLIM and the series of derived indicators for histopathological evaluation of ESCC. As there is an increasing interest in developing multiphoton endoscopes and multiphoton FLIM systems for clinical use, the proposed method would probably allow noninvasive, label-free, and multidimensional histological detection and grading of ESCC in the future.

En-face polarization-sensitive optical coherence tomography to characterize early-stage esophageal cancer and determine tumor margin

Current imaging tools are insufficiently sensitive to the early diagnosis of esophageal squamous cell carcinoma (ESCC). The application of polarization-sensitive optical coherence tomography (PS-OCT) to detect tumor-stroma interaction is an interesting issue in cancer diagnosis. In this translational study, we found that en-face PS-OCT effectively characterizes protruding, flat, and depressive type ESCC regardless of animal or human specimens. In addition, the tumor contour and margin could also be drawn and determined on a broad en-face view. The determined tumor margin could be in the proximity of 2 mm to the actual tumor margin, which was proved directly using histology.

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis

BACKGROUND

Early screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, early EC is difficult to diagnose by a routine endoscopic examination. Therefore, convolutional neural network (CNN)-based artificial intelligence (AI) has become a very promising method in the diagnosis of early EC using endoscopic images. The aim of this study was to evaluate the diagnostic performance of CNN-based AI for detecting early EC based on endoscopic images.

METHODS

A comprehensive search was performed to identify relevant English articles concerning CNN-based AI in the diagnosis of early EC based on endoscopic images (from the date of database establishment to April 2022). The pooled sensitivity (SEN), pooled specificity (SPE), positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR) with 95% confidence interval (CI), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) for the accuracy of CNN-based AI in the diagnosis of early EC based on endoscopic images were calculated. We used the I² test to assess heterogeneity and investigated the source of heterogeneity by performing meta-regression analysis. Publication bias was assessed using Deeks' funnel plot asymmetry test.

RESULTS

Seven studies met the eligibility criteria. The SEN and SPE were 0.90 (95% confidence interval [CI]: 0.82-0.94) and 0.91 (95% CI: 0.79-0.96), respectively. The LR+ of the malignant ultrasonic features was 9.8 (95% CI: 3.8-24.8) and the LR- was 0.11 (95% CI: 0.06-0.21), revealing that CNN-based AI exhibited an excellent ability to confirm or exclude early EC on endoscopic images. Additionally, SROC curves showed that the AUC of the CNN-based AI in the diagnosis of early EC based on endoscopic images was 0.95 (95% CI: 0.93-0.97), demonstrating that CNN-based AI has good diagnostic value for early EC based on endoscopic images.

CONCLUSIONS

Based on our meta-analysis, CNN-based AI is an excellent diagnostic tool with high sensitivity, specificity, and AUC in the diagnosis of early EC based on endoscopic images.

Endoscopic features of esophageal high-grade intraepithelial neoplasia dominated by cytological atypia

Little is known about esophageal high-grade intraepithelial neoplasia dominated by cytological atypia (HGINc). We aimed to elucidate the endoscopic features of HGINc compared with esophageal high-grade intraepithelial neoplasia dominated by architectural atypia (HGINa). All patients pathologically diagnosed as esophageal high-grade intraepithelial neoplasia after endoscopic submucosal dissection at our center between January 2018 and December 2019 were included in this study. According to the pathological diagnosis, the patients were divided into two groups: HGINa group and HGINc group. Basic characteristics and endoscopic information were collected in detail. Data were analyzed statistically. Binary logistic regression was performed and a predictive model for HGINc was established. Then we evaluated its predictive value and built a nomogram for clinical application. A total of 175 patients were included in this study (126 with HGINa and 49 with HGINc). Among 228 lesions found in all patients, there were 148 HGINa and 80 HGINc. The independent relevant factors for HGINc were tobacco and alcohol usage, color, and gross type. To predict risk of HGINc, a three-factor model (TFM) was established with a highest area under curve (AUC) as 0.869 (95% CI, 0.852, 0.939). When the cut-off value was set as 0.3569184, the diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for HGINc was 81.14%, 88.75%, 77.03%, 67.62%, and 92.68%, respectively. HGINc differs greatly in endoscopic features from HGINa in our study. It's important to reduce misdiagnosis that our model was established with good predictive value for clinical application.

Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence

Simple Summary

Esophageal cancer (EC) has a poor prognosis when the diagnosis is delayed, but curative treatment is possible if the diagnosis is timely. The disease subtly progresses before symptoms prompt patients to seek medical attention. Effective pre-symptomatic screening strategies may improve the outcome of the disease. Recent evidence provided insights into early diagnosis of EC via blood tests, advanced endoscopic imaging, and artificial intelligence. Accordingly, we reviewed available strategies to diagnose early EC.

Abstract

Esophageal cancer (EC) is the seventh most common cancer and the sixth cause of cancer death worldwide. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) account for up to 90% and 20% of all ECs, respectively. Clinical symptoms such as dysphagia, odynophagia, and bolus impaction occur late in the natural history of the disease, and the diagnosis is often delayed. The prognosis of ESCC and EAC is poor in advanced stages, being survival rates less than 20% at five years. However, when the diagnosis is achieved early, curative treatment is possible, and survival exceeds 80%. For these reasons, mass screening strategies for EC are highly desirable, and several options are currently under investigation. Blood biomarkers offer an inexpensive, non-invasive screening strategy for cancers, and novel technologies have allowed the identification of candidate markers for EC. The esophagus is easily accessible via endoscopy, and endoscopic imaging represents the gold standard for cancer surveillance. However, lesion recognition during endoscopic procedures is hampered by interobserver variability. To fill this gap, artificial intelligence (AI) has recently been explored and provided encouraging results. In this review, we provide a summary of currently available options to achieve early diagnosis of EC, focusing on blood biomarkers, advanced endoscopy, and AI.