Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning.

BACKGROUND: The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application. METHODS: We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations. RESULTS: The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration. CONCLUSIONS: We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.

Distribution and density of tertiary lymphoid structures predict clinical outcome in intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: The prognostic value and clinical relevance of tertiary lymphoid structures (TLSs) in intrahepatic cholangiocarcinoma (iCCA) remain unclear. Thus, we aimed to investigate the prognostic value and functional involvement of TLSs in iCCA. METHODS: We retrospectively included 962 patients from 3 cancer centers across China. The TLSs at different anatomic subregions were quantified and correlated with overall survival (OS) by Cox regression and Kaplan-Meier analyses. Multiplex immunohistochemistry (mIHC) was applied to characterize the composition of TLSs in 39 iCCA samples. RESULTS: A quaternary TLS scoring system was established for the intra-tumor region (T score) and peri-tumor region (P score) respectively. T scores positively correlated with favorable prognosis (p <0.001), whereas a high P score signified worse survival (p <0.001). mIHC demonstrated that both T follicular helper and regulatory T cells were significantly increased in intra-tumoral TLSs compared to peri-tumoral counterparts (p <0.05), and regulatory T cell frequencies within intra-tumoral TLSs were positively associated with P score (p <0.05) rather than T score. Collectively, the combination of T and P scores stratified iCCAs into 4 immune classes with distinct prognoses (p <0.001) that differed in the abundance and distribution pattern of TLSs. Patients displaying an immune-active pattern had the lowest risk, with 5-year OS rates of 68.8%, whereas only 3.4% of patients with an immune-excluded pattern survived at 5 years (p <0.001). The C-index of the immune class was statistically higher than the TNM staging system (0.73 vs. 0.63, p <0.001). These results were validated in an internal and 2 external cohorts. CONCLUSIONS: The spatial distribution and abundance of TLSs significantly correlated with prognosis and provided a useful immune classification for iCCA. T follicular helper and regulatory T cells may play a critical role in determining the functional orientation of spatially different TLSs. LAY SUMMARY: Tertiary lymphoid structures (TLSs) are associated with favorable prognosis in a number of cancers. However, their role in intrahepatic cholangiocarcinoma (iCCA) remains unclear. Herein, we comprehensively evaluated the spatial distribution, abundance, and cellular composition of TLSs in iCCA, and revealed the opposite prognostic impacts of TLSs located within or outside the tumor. This difference could be mediated by the different immune cell subsets present within the spatially distinct TLSs. Based on our analysis, we were able to stratify iCCAs into 4 immune subclasses associated with varying prognoses.

Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video).

BACKGROUND AND AIMS: Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection (CAD) using a deep neural network (DNN) to localize and identify early ESCC under conventional endoscopic white-light imaging. METHODS: We collected 2428 (1332 abnormal, 1096 normal) esophagoscopic images from 746 patients to set up a novel DNN-CAD system in 2 centers and prepared a validation dataset containing 187 images from 52 patients. Sixteen endoscopists (senior, mid-level, and junior) were asked to review the images of the validation set. The diagnostic results, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared between the DNN-CAD system and endoscopists. RESULTS: The receiver operating characteristic curve for DNN-CAD showed that the area under the curve was >96%. For the validation dataset, DNN-CAD had a sensitivity, specificity, accuracy, PPV, and NPV of 97.8%, 85.4%, 91.4%, 86.4%, and 97.6%, respectively. The senior group achieved an average diagnostic accuracy of 88.8%, whereas the junior group had a lower value of 77.2%. After referring to the results of DNN-CAD, the average diagnostic ability of the endoscopists improved, especially in terms of sensitivity (74.2% vs 89.2%), accuracy (81.7% vs 91.1%), and NPV (79.3% vs 90.4%). CONCLUSIONS: The novel DNN-CAD system used for screening of early ESCC has high accuracy and sensitivity, and can help endoscopists to detect lesions previously ignored under white-light imaging.

Bio-Reduction of Graphene Oxide Using Sulfate-Reducing Bacteria and Its Implication on Anti-Biocorrosion.

In this paper, we developed an environmental friendly, cost effective, simple and green approach to reduce graphene oxide (GO) by a sulfate-reducing bacterium Desulfovibrio desulfuricans. The D. desulfuricans reduces exfoliated GO to reduced graphene oxide (rGO) at 25 °C in an aqueous solution without any toxic and environmentally harmful reducing agents. The rGO was characterized with X-ray Diffraction, Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, Transmission Electron Microscope, X-ray Photoelectron Spectroscopy and Raman Spectroscopy. The analysis results showed that rGO had excellent properties and multi-layer graphene sheets structure. Furthermore, we demonstrated that D. desulfuricans, one of the primary bacteria responsible for the biocorrosion of various metals, might reduce GO to rGO on the surface of copper and prevented the corrosion of copper, which confirmed that electrophoretic deposition of GO on the surface of metals had great potential on the anti-biocorrosion applications.

Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging.

BACKGROUND: Non-magnifying endoscopy with narrow-band imaging (NM-NBI) has been frequently used in routine screening of esophagus squamous cell carcinoma (ESCC). The performance of NBI for screening of early ESCC is, however, significantly affected by operator experience. Artificial intelligence may be a unique approach to compensate for the lack of operator experience. AIM: To construct a computer-aided detection (CAD) system for application in NM-NBI to identify early ESCC and to compare it with our previously reported CAD system with endoscopic white-light imaging (WLI). METHODS: A total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images were collected from three institutions (Zhongshan Hospital of Fudan University, Xuhui Hospital, and Kiang Wu Hospital) as the training dataset, and 316 pairs of images, each pair including images obtained by WLI and NBI (same part), were collected for validation. Twenty endoscopists participated in this study to review the validation images with or without the assistance of the CAD systems. The diagnostic results of the two CAD systems and improvement in diagnostic efficacy of endoscopists were compared in terms of sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. RESULTS: The area under receiver operating characteristic curve for CAD-NBI was 0.9761. For the validation dataset, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CAD-NBI were 91.0%, 96.7%, 94.3%, 95.3%, and 93.6%, respectively, while those of CAD-WLI were 98.5%, 83.1%, 89.5%, 80.8%, and 98.7%, respectively. CAD-NBI showed superior accuracy and specificity than CAD-WLI (P = 0.028 and P ≤ 0.001, respectively), while CAD-WLI had higher sensitivity than CAD-NBI (P = 0.006). By using both CAD-WLI and CAD-NBI, the endoscopists could improve their diagnostic efficacy to the highest level, with accuracy, sensitivity, and specificity of 94.9%, 92.4%, and 96.7%, respectively. CONCLUSION: The CAD-NBI system for screening early ESCC has higher accuracy and specificity than CAD-WLI. Endoscopists can achieve the best diagnostic efficacy using both CAD-WLI and CAD-NBI.

Effects of thermal fatigue on shear punch strength of tooth-colored restoratives.

AIMS: This study investigated the effect of thermal fatigue on the shear strength of a range of tooth-colored restorative materials including giomers, zirconia-reinforced glass ionomer cement (GIC), nano-particle resin-modified GIC, highly viscous GICs, and composite resin. MATERIALS AND METHODS: Twenty specimens of each material were fabricated in standardized washers (17 mm outer diameter, 9 mm internal diameter, 1 mm thick). The specimens were cured, stored in 100% humidity at 37.5°C for 24 h, and randomly divided into two groups of 10. Group A specimens were nonthermocycled (NT) and stored in distilled water at 37°C for 168 h. Group B specimens were thermocycled (TC) for 10,000 cycles (168 h) with baths X, Y, and Z adjusted to 35°C, 15°C, and 45°C, respectively. Each cycle had dwell times of 28 s in X, and 2s in Y/Z in the order XYXZ. Specimens then underwent shear punch testing at a crosshead speed of 0.5 mm/min with a 2 kN load cell. Statistical analysis of shear strength was done using t-test and two-way ANOVA/Scheffe's post hoc test at significance level P < 0.05. RESULTS: The effect of thermal fatigue on shear strength was material dependent. Except for the "sculptable" giomer (Beautifil II) and a highly viscous GIC (Fuji IX GP Fast), no significant differences in shear strength were generally observed between the NT and TC groups. For both groups, the composite resin (Filtek Z250XT) had the highest shear strength while the zirconia-reinforced (zirconomer) and a highly viscous GIC (Ketac Molar Quick) had the lowest. CONCLUSIONS: The effect of thermocycling on shear strength was material dependent. Thermal fatigue, however, did not significantly influence the shear strength of most materials assessed. The "sculptable" composite and giomer were significantly stronger than the other materials evaluated. Shear strength of the "flowable" injectable hybrid giomer was intermediate between the composite and GICs.

Expression of albumin, IGF-1, IGFBP-3 in tumor tissues and adjacent non-tumor tissues of hepatocellular carcinoma patients with cirrhosis.

AIM: To explore the expression of albumin (ALB), insulin-like growth factor (IGF)-1, and insulin-like growth factor binding protein (IGFBP)-3 in tumor tissues and adjacent non-tumor tissues of hepatocellular carcinoma (HCC) patients with cirrhosis. METHODS: Twenty-four HCC patients with cirrhosis who underwent hepatectomy were studied. ALB mRNA, IGF-1 mRNA, and IGFBP-3 mRNA in liver tissues (including tumor tissues and adjacent non-tumor tissues) were detected by reverse transcriptase-polymerase chain reaction (RT-PCR). Liver Ki67 immunohistochemistry staining was studied. At the same time, 12 patients with cholelithiasis or liver angioma who underwent operation were segregated as normal control. RESULTS: In HCC patients with cirrhosis, hepatic ALB mRNA, IGF-1 mRNA, and IGFBP-3 mRNA of tumor tissues or adjacent non-tumor tissues were lower than the normal liver tissues, while in tumor tissues, hepatic ALB mRNA and IGFBP-3 mRNA were lower, hepatic IGF-1 mRNA was higher than in adjacent non-tumor tissues. Liver Ki67 labeling index (Ki67 LI) in tumor tissues or adjacent non-tumor tissues were higher than that in the normal liver tissues, while in tumor tissues it was higher than that in adjacent non-tumor tissues. CONCLUSION: Imbalance of IGF-1 and IGFBP-3 may play a role in hepatocarcinogenesis and tumor development of liver cirrhosis patients.

[Application of Onodera prognostic nutrition index in prognostic evaluation of elderly patients with colorectal cancer].

OBJECTIVE: To study the applied valuation of Onodera prognostic nutrition index (Onodera index) in elderly patients with colorectal cancer. METHODS: Onodera indexes of 163 elderly patients with colorectal cancer were calculated and these patients were divided into better-nourished group (Onodera index ≥45) and under-nourished group (Onodera index <45). Correlations of Onodera index with general data, operation type, postoperative complication, recovery of gastrointestinal function, clinicopathological feature and prognosis were analyzed. Cox proportional hazards model was also established to identify the independent prognostic factors for prognosis of elderly patients with colorectal cancer. RESULTS: Patients in better-nourished group had significantly higher radical resection rate [90.9% (70/77) vs. 62.8% (54/86), P<0.01], lower postoperative complication rate [17.1% (12/70) vs. 53.7% (29/54), P<0.01] and earlier postoperative defecation [(3.09±1.14) d vs. (3.43±1.98) d, P<0.05] than those in under-nourished group. Onodera index was found to be related to age, tumor location, tumor size, and operation type (all P<0.05). Better-nourished group had significantly better survival than worse-nourished group (5-year survival rate: 64% vs. 24%, P<0.01). Onodera index was identified as an independent prognostic factor for elderly patients with colorectal cancer (RR=0.888, 95%CI:0.800-0.985, P=0.025). CONCLUSION: Onodera index is a valuable clinical marker in preoperative estimation as well as prognosis prediction for elderly patients with colorectal cancer.