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.

Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism.

The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an implicit fairness learning approach to build a fairer ophthalmology AI (called FairerOPTH) that mitigates sex (biological attribute) and age biases in AI diagnosis of eye diseases. Specifically, FairerOPTH incorporates the causal relationship between fundus features and eye diseases, which is relatively independent of sensitive attributes such as race, sex, and age. We demonstrate on a large and diverse collected dataset that FairerOPTH significantly outperforms several state-of-the-art approaches in terms of diagnostic accuracy and fairness for 38 eye diseases in ultra-widefield imaging and 16 eye diseases in narrow-angle imaging. This work demonstrates the significant potential of implicit fairness learning in promoting equitable treatment for patients regardless of their sex or age.

Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration.

Fluorescence microscopy-based image restoration has received widespread attention in the life sciences and has led to significant progress, benefiting from deep learning technology. However, most current task-specific methods have limited generalizability to different fluorescence microscopy-based image restoration problems. Here, we seek to improve generalizability and explore the potential of applying a pretrained foundation model to fluorescence microscopy-based image restoration. We provide a universal fluorescence microscopy-based image restoration (UniFMIR) model to address different restoration problems, and show that UniFMIR offers higher image restoration precision, better generalization and increased versatility. Demonstrations on five tasks and 14 datasets covering a wide range of microscopy imaging modalities and biological samples demonstrate that the pretrained UniFMIR can effectively transfer knowledge to a specific situation via fine-tuning, uncover clear nanoscale biomolecular structures and facilitate high-quality imaging. This work has the potential to inspire and trigger new research highlights for fluorescence microscopy-based image restoration.

Self-Supervised Digital Histopathology Image Disentanglement for Arbitrary Domain Stain Transfer.

Diagnosis of cancerous diseases relies on digital histopathology images from stained slides. However, the staining varies among medical centers, which leads to a domain gap of staining. Existing generative adversarial network (GAN) based stain transfer methods highly rely on distinct domains of source and target, and cannot handle unseen domains. To overcome these obstacles, we propose a self-supervised disentanglement network (SDN) for domain-independent optimization and arbitrary domain stain transfer. SDN decomposes an image into features of content and stain. By exchanging the stain features, the staining style of an image is transferred to the target domain. For optimization, we propose a novel self-supervised learning policy based on the consistency of stain and content among augmentations from one instance. Therefore, the process of training SDN is independent on the domain of training data, and thus SDN is able to tackle unseen domains. Exhaustive experiments demonstrate that SDN achieves the top performance in intra-dataset and cross-dataset stain transfer compared with the state-of-the-art stain transfer models, while the number of parameters in SDN is three orders of magnitude smaller parameters than that of compared models. Through stain transfer, SDN improves AUC of downstream classification model on unseen data without fine-tuning. Therefore, the proposed disentanglement framework and self-supervised learning policy have significant advantages in eliminating the stain gap among multi-center histopathology images.

Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis.

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose a lesion-decoupling-based segmentation (LDS) network for assisting early cancer diagnosis. We introduce a plug-and-play module called self-sampling similar feature disentangling module (FDM) to obtain accurate lesion boundaries. Then, we propose a feature separation loss (FSL) function to separate pathological features from normal ones. Moreover, since physicians make diagnoses with multimodal data, we propose a multimodal cooperative segmentation network with two different modal images as input: white-light images (WLIs) and narrowband images (NBIs). Our FDM and FSL show a good performance for both single-modal and multimodal segmentations. Extensive experiments on five backbones prove that our FDM and FSL can be easily applied to different backbones for a significant lesion segmentation accuracy improvement, and the maximum increase of mean Intersection over Union (mIoU) is 4.58. For colonoscopy, we can achieve up to mIoU of 91.49 on our Dataset A and 84.41 on the three public datasets. For esophagoscopy, mIoU of 64.32 is best achieved on the WLI dataset and 66.31 on the NBI dataset.

ClusterNet: a clustering distributed prior embedded detection network for early-stage esophageal squamous cell carcinoma diagnosis.

BACKGROUND: Early and accurate diagnosis of esophageal squamous cell carcinoma (ESCC) is important for reducing mortality. Analyzing intrapapillary capillary loops' (IPCLs) patterns on magnification endoscopy with narrow band imaging (ME-NBI) has been demonstrated effective in the diagnosis of early-stage ESCC. However, even experienced endoscopists may face difficulty in finding and classifying countless IPCLs on ME-NBI. PURPOSE: We propose a novel clustering prior embedded detection network: ClusterNet. ClusterNet is capable of analyzing the distribution of IPCLs on ME-NBI automatically and enables endoscopists to overview multiple types of visualization. With ClusterNet assisting, endoscopists may observe ME-NBI images more efficiently, thus they may also predict the pathology and make medical decisions more easily. METHODS: We propose the first large-scale ME-NBI dataset with fine-grained annotations by consensus of expert endoscopists. The dataset is splitted into a training set and an independent testing set based on patients. With two strategies for embedding, ClusterNet can automatically take the clustering effect into consideration. Prior to this work, none of the existing approaches take the clustering effect, which is rather important in classifying the IPCLs, into account. RESULTS: ClusterNet achieves an average precision of 81.2% and an average recall of 90.0% for the detection of IPCLs patterns on each patient of the independent testing set. We also compare ClusterNet with other state-of-the-art detection approaches. The performance of ClusterNet with embedding strategies is consistently superior to that of other approaches in terms of average precision, recall and F2-Score. CONCLUSIONS: Experiments demonstrate that our proposed method is able to detect almost all the IPCLs patterns on ME-NBI and classify them according to the Japanese Endoscopic Society (JES) classification accurately.

Adaptive Cross Entropy for ultrasmall object detection in Computed Tomography with noisy labels.

Conventional size object detection has been extensively studied, whereas researches concerning ultrasmall object detection are rare due to lack of dataset. Here, considering that the stapes in the ear is the smallest bone in our body, we have collected the largest stapedial otosclerosis detection dataset from 633 stapedial otosclerosis patients and 269 normal cases to promote this direction. Nevertheless, noisy classification labels in our dataset are inevitable due to various subjective and objective factors, and this situation prevails in various fields. In this paper, we propose a novel and general noise tolerant loss function named Adaptive Cross Entropy (ACE) which needs no fine-tuning of hyperparameters for training with noisy labels. We provide both theoretical and empirical analyses for the proposed ACE loss and demonstrate its effectiveness in multiple public datasets. Besides, we find high-resolution representations crucial for ultrasmall object detection and present an auxiliary backbone called W-Net to address it accordingly. Extensive experiments demonstrate that the proposed ACE loss is able to boost the diagnosis performance under noisy label setting by a large margin. Furthermore, our W-Net can help extract sufficient high-resolution representations specialized for ultrasmall objects and achieve even better results. Hopefully, our work could provide more clues for future research on ultrasmall object detection and learning with noisy labels.

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.

The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography.

BACKGROUND: The purpose of this study was to explore the common characteristics of fenestral otosclerosis (OS) which are misdiagnosed, and develop a deep learning model for the diagnosis of fenestral OS based on temporal bone high-resolution computed tomography scans. METHODS: We conducted a study to explicitly analyze the clinical performance of otolaryngologists in diagnosing fenestral OS and developed an explainable deep learning model using 134,574 temporal bone high-resolution computed tomography (HRCT) slices collected from 1,294 patients for the automatic diagnosis of fenestral OS. We prospectively created an external test set with 31,774 CT slices from 144 patients, which contained 86 fenestral OS ears and 202 normal ears and used it to evaluate the performance of our otosclerosis-Logical Neural Network (LNN) model to assess its potential clinical utility. In addition, we compared the diagnostic acumen of seven otolaryngologists with the otosclerosis-LNN approach in the clinical test set, which was mixed with 78 fenestral OS and 62 normal ears. Finally, to evaluate the assisting value of the model, the seven participants were again invited to classify all cases in the clinical test set after referring to the diagnostic results of the model, to which they were blinded. RESULTS: The diagnostic performance of otologists was not satisfactory, and those CT samples which were misdiagnosed had similar characteristics. Based on this finding, we defined three subtypes of fenestral OS lesions that are suitable for clinical diagnosis guidance: "focal", "transitional", and "typical" fenestral OS. The most encouraging result is that the model achieved an area under the curve (AUC) of 99.5% (per-ear-sensitivity of 96.4%, per-ear-specificity of 98.9%) on the prospective unknown external test. Furthermore, we used this model to assist otologists and observed a consistent and significant improvement in diagnostic performance, especially for the newly defined focal and transitional fenestral OS, which led to the initial high misdiagnosis rate. CONCLUSIONS: Our findings of the fine-grained classification of fenestral OS could have implications for future diagnosis and prevention programs. In addition, our deep OS localization network is an effective approach providing assistance to otologists to deal with the significant challenge of the misdiagnosis of fenestral OS.

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.

Hierarchical porous and hydrophilic metal-organic frameworks with enhanced enzyme activity.

Metal-organic frameworks (MOFs) for enzyme encapsulation-induced biomimetic mineralization under mild reaction conditions are commonly microporous and hydrophobic, which result in a rather high mass transfer resistance of the reactants and restrain the enzyme catalytic activity. Herein, we prepared a type of hierarchical porous and hydrophilic MOF through the biomimetic mineralization of enzymes, zinc ions, 2-methylimidazole, and lithocholic acid. The hierarchical porous structure accelerated the diffusion process of the reactants and the increased hydrophilicity conferred interfacial activity and increased the enzyme catalytic activity. The immobilized enzyme retained higher catalytic activity than the free enzyme and exhibited enhanced resistance to alkaline, organic, and high-temperature conditions. The nanobiocatalyst was reusable and showed long-term storage stability.

Sustainable separation of bio-based cadaverine based on carbon dioxide capture by forming carbamate.

Bio-based cadaverine, manufactured by the decarboxylation of l-lysine, is an important raw material. However, the extractive-distillation separation and purification of cadaverine from bioconversion fluids require high energy consumption and leads to the loss of self-released carbon dioxide during the decarboxylation of l-lysine. This study focuses on the green and sustainable separation of bio-based cadaverine based on the capture of self-released carbon dioxide by cadaverine forming carbamate. Results showed that granular-activated carbon JK1 shows the best decolorization efficiency and achieves a higher cadaverine yield. After three times of solventing-out crystallization, refined cadaverine carbamate with 99.1% purity and total 57.48% yield was obtained. It was also found that the refined cadaverine carbamate consists of mixed crystals having numerous structural forms that can easily dissociate carbon dioxide. Furthermore, the amine carbamate strategy may be of great value for the development of a green and sustainable separation mode of bio-based amines and carbon dioxide capture.

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.

Artificial Nanometalloenzymes for Cooperative Tandem Catalysis.

Artificial metalloenzymes that combine the advantages of natural enzymes and metal catalysts have been getting more attention in research. As a proof of concept, an artificial nanometalloenzyme (CALB-Shvo@MiMBN) was prepared by co-encapsulation of metallo-organic catalyst and enzyme in a soft nanocomposite consisting of 2-methylimidazole, metal ions, and biosurfactant in mild reaction conditions using a one-pot self-assembly method. The artificial nanometalloenzyme with lipase acted as the core, and the metallo-organic catalyst embedded in micropore exhibited a spherical structure of 30-50 nm in diameter. The artificial nanometalloenzyme showed high catalytic efficiency in the dynamic kinetic resolution of racemic primary amines or secondary alcohols compared to the one-pot catalytic reaction of immobilized lipase and free metallo-organic catalyst. This artificial nanometalloenzyme holds great promise for integrated enzymatic and heterogeneous catalysis.

Num1 versus NuMA: insights from two functionally homologous proteins.

In both animals and fungi, spindle positioning is dependent upon pulling forces generated by cortically anchored dynein. In animals, cortical anchoring is accomplished by a ternary complex containing the dynein-binding protein NuMA and its cortical attachment machinery. The same function is accomplished by Num1 in budding yeast. While not homologous in primary sequence, NuMA and Num1 appear to share striking similarities in their mechanism of function. Here, we discuss evidence supporting that Num1 in fungi is a functional homolog of NuMA due to their similarity in domain organization and role in the generation of cortical pulling forces.

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.

An in vitro Microscopy-based Assay for Microtubule-binding and Microtubule-crosslinking by Budding Yeast Microtubule-associated Protein.

In this protocol, we describe a simple microscopy-based method to assess the interaction of a microtubule-associated protein (MAP) with microtubules. The interaction between MAP and microtubules is typically assessed by a co-sedimentation assay, which measures the amount of MAP that co-pellets with microtubules by centrifugation, followed by SDS-PAGE analysis of the supernatant and pellet fractions. However, MAPs that form large oligomers tend to pellet on their own during the centrifugation step, making it difficult to assess co-sedimentation. Here we describe a microscopy-based assay that measures microtubule binding by direct visualization using fluorescently-labeled MAP, solving the limitations of the co-sedimentation assay. Additionally, we recently reported quantification of microtubule bundling by measuring the thickness of individual microtubule structures observed in the microscopy-based assay, making the protocol more advantageous than the traditional microtubule co-pelleting assay.

Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting.

Traditional image resizing methods, such as uniform scaling and content-aware image retargeting, are designed to preserve the visually salient contents of an image while resizing it. In this paper, we propose a novel image resizing approach called recognition-oriented image retargeting. Its goal is to preserve the distinctive local features for recognition instead of the traditional visual saliency during resizing. Moreover, we also apply our approach to image matching and image retrieval applications to verify its performance. Meanwhile, using our approach to these applications is able to solve some of the challenging problems in their fields. In image matching application, we find that our approach shows promising preservation of local feature descriptors. In image retrieval task, extensive experiments on Oxford5K, Holidays, Paris, and Flickr100k data sets demonstrate that our approach consistently outperforms other image retargeting methods by large margins in the aspects of retrieval precision and query bits.

Strike kinematics and performance in juvenile ball pythons (Python regius).

The rapid strike of snakes has interested researchers for decades. Although most work has focused on the strike performance of vipers, recent work has shown that other snakes outside of the Viperidae can strike with the same velocities and accelerations. However, to date all of these examples focus on performance in adult snakes. Here, we use high-speed video to measure the strike kinematics and performance of 10 juvenile (<6 months of age) ball pythons, Python regius. We find that juvenile P. regius strike at levels comparable to larger snakes, but with shorter durations and over shorter distances. We conclude that the juvenile P. regius maintain performance likely through manipulation of the axial musculature and accompanying elastic tissues, and that this is a first step to understanding ontogenetic changes in behavior and a potential avenue for understanding how captivity may also impact behavior.

[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.