[Ascending colon cancer with submucosal tumor-like morphology arising near ileal valve lipohyperplasia:a case report].

An 89-year-old man was diagnosed with a submucosal tumor suspected to be a lipoma and was followed up for 6 years. The patient was admitted to the hospital because of increased tumor size and morphological changes despite negative bioptic findings. The lesion was diagnosed as an advanced adenocarcinoma of the ascending colon (cT3N0M0, cStage IIa). Laparoscopic-assisted right hemicolectomy with D3 lymph node dissection was performed. Pathological diagnosis of a surgically resected specimen revealed adenocarcinoma with lipohyperplasia (pT3N2aM0, pStage IIIb). Reports of colon cancer accompanied by colonic lipomas or lipohyperplasia are limited. This case showed an interesting submucosal tumor-like morphology because the cancer developed at the base of the lipohyperplasia and grew and spread below it.

Dual ensemble system for polyp segmentation with submodels adaptive selection ensemble.

Colonoscopy is one of the main methods to detect colon polyps, and its detection is widely used to prevent and diagnose colon cancer. With the rapid development of computer vision, deep learning-based semantic segmentation methods for colon polyps have been widely researched. However, the accuracy and stability of some methods in colon polyp segmentation tasks show potential for further improvement. In addition, the issue of selecting appropriate sub-models in ensemble learning for the colon polyp segmentation task still needs to be explored. In order to solve the above problems, we first implement the utilization of multi-complementary high-level semantic features through the Multi-Head Control Ensemble. Then, to solve the sub-model selection problem in training, we propose SDBH-PSO Ensemble for sub-model selection and optimization of ensemble weights for different datasets. The experiments were conducted on the public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, ETIS-LaribPolypDB and PolypGen. The results show that the DET-Former, constructed based on the Multi-Head Control Ensemble and the SDBH-PSO Ensemble, consistently provides improved accuracy across different datasets. Among them, the Multi-Head Control Ensemble demonstrated superior feature fusion capability in the experiments, and the SDBH-PSO Ensemble demonstrated excellent sub-model selection capability. The sub-model selection capabilities of the SDBH-PSO Ensemble will continue to have significant reference value and practical utility as deep learning networks evolve.

Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network.

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods.

Noninvasive Stratification of Colon Cancer by Multiplex PET Imaging.

PURPOSE: The current approach for molecular subtyping of colon cancer relies on gene expression profiling, which is invasive and has limited ability to reveal dynamics and spatial heterogeneity. Molecular imaging techniques, such as PET, present a noninvasive alternative for visualizing biological information from tumors. However, the factors influencing PET imaging phenotype, the suitable PET radiotracers for differentiating tumor subtypes, and the relationship between PET phenotypes and tumor genotype or gene expression-based subtyping remain unknown. EXPERIMENTAL DESIGN: In this study, we conducted 126 PET scans using four different metabolic PET tracers, [18F]fluorodeoxy-D-glucose ([18F]FDG), O-(2-[18F]fluoroethyl)-l-tyrosine ([18F]FET), 3'-deoxy-3'-[18F]fluorothymidine ([18F]FLT), and [11C]acetate ([11C]ACE), using a spectrum of five preclinical colon cancer models with varying genetics (BMT, AKPN, AK, AKPT, KPN), at three sites (subcutaneous, orthograft, autochthonous) and at two tumor stages (primary vs. metastatic). RESULTS: The results demonstrate that imaging signatures are influenced by genotype, tumor environment, and stage. PET imaging signatures exhibited significant heterogeneity, with each cancer model displaying distinct radiotracer profiles. Oncogenic Kras and Apc loss showed the most distinctive imaging features, with [18F]FLT and [18F]FET being particularly effective, respectively. The tissue environment notably impacted [18F]FDG uptake, and in a metastatic model, [18F]FET demonstrated higher uptake. CONCLUSIONS: By examining factors contributing to PET-imaging phenotype, this study establishes the feasibility of noninvasive molecular stratification using multiplex radiotracer PET. It lays the foundation for further exploration of PET-based subtyping in human cancer, thereby facilitating noninvasive molecular diagnosis.

Facile synthesis of manganese-hafnium nanocomposites for multimodal MRI/CT imaging and in vitro photodynamic therapy of colon cancer.

Precise diagnosis of complex and soft tumors is challenging, which limits appropriate treatment options to achieve desired therapeutic outcomes. However, multifunctional nano-sized contrast enhancement agents based on nanoparticles improve the diagnosis accuracy of various diseases such as cancer. Herein, a facile manganese-hafnium nanocomposites (Mn3O4-HfO2 NCs) system was designed for bimodal magnetic resonance imaging (MRI)/computed tomography (CT) contrast enhancement with a complimentary function of photodynamic therapy. The solvothermal method was used to fabricate NCs, and the average size of Mn3O4 NPs and Mn3O4-HfO2 NCs was about 7 nm and 15 nm, respectively, as estimated by TEM. Dynamic light scattering results showed good dispersion and high negative (-33 eV) zeta potential, indicating excellent stability in an aqueous medium. Mn3O4-HfO2 NCs revealed negligible toxic effects on the NCTC clone 929 (L929) and mouse colon cancer cell line (CT26), demonstrating promising biocompatibility. The synthesized Mn3O4-HfO2 NCs exhibit significant enhancement in T1-weighted magnetic resonance imaging (MRI) and X-ray computed tomography (CT), indicating the appropriateness for dual-modal MRI/CT molecular imaging probes. Moreover, ultra-small Mn3O4-HfO2 NCs show good relaxivities for MRI/CT. These nanoprobes Mn3O4-HfO2 NCs further possessed outstanding reactive oxygen species (ROS) generation ability under minute ultraviolet light (6 mW·cm-2) to ablate the colon cancer cells in vitro. Therefore, the designed multifunctional Mn3O4-HfO2 NCs were ideal candidates for cancer diagnosis and photodynamic therapy.

Magnetic resonance imaging-based approaches for detecting the efficacy of combining therapy following VEGFR-2 and PD-1 blockade in a colon cancer model.

BACKGROUND: Angiogenesis inhibitors have been identified to improve the efficacy of immunotherapy in recent studies. However, the delayed therapeutic effect of immunotherapy poses challenges in treatment planning. Therefore, this study aims to explore the potential of non-invasive imaging techniques, specifically intravoxel-incoherent-motion diffusion-weighted imaging (IVIM-DWI) and blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI), in detecting the anti-tumor response to the combination therapy involving immune checkpoint blockade therapy and anti-angiogenesis therapy in a tumor-bearing animal model. METHODS: The C57BL/6 mice were implanted with murine MC-38 cells to establish colon cancer xenograft model, and randomly divided into the control group, anti-PD-1 therapy group, and combination therapy group (VEGFR-2 inhibitor combined with anti-PD-1 antibody treatment). All mice were imaged before and, on the 3rd, 6th, 9th, and 12th day after administration, and pathological examinations were conducted at the same time points. RESULTS: The combination therapy group effectively suppressed tumor growth, exhibiting a significantly higher tumor inhibition rate of 69.96% compared to the anti-PD-1 group (56.71%). The f value and D* value of IVIM-DWI exhibit advantages in reflecting tumor angiogenesis. The D* value showed the highest correlation with CD31 (r = 0.702, P = 0.001), and the f value demonstrated the closest correlation with vessel maturity (r = 0.693, P = 0.001). While the BOLD-MRI parameter, R2* value, shows the highest correlation with Hif-1α(r = 0.778, P < 0.001), indicating the capability of BOLD-MRI to evaluate tumor hypoxia. In addition, the D value of IVIM-DWI is closely related to tumor cell proliferation, apoptosis, and infiltration of lymphocytes. The D value was highly correlated with Ki-67 (r = - 0.792, P < 0.001), TUNEL (r = 0.910, P < 0.001) and CD8a (r = 0.918, P < 0.001). CONCLUSIONS: The combination of VEGFR-2 inhibitors with PD-1 immunotherapy shows a synergistic anti-tumor effect on the mouse colon cancer model. IVIM-DWI and BOLD-MRI are expected to be used as non-invasive approaches to provide imaging-based evidence for tumor response detection and efficacy evaluation.

Characterizing colon cancer stages through optical polarimetry-assisted digital staining.

Tissue polarimetry has been gaining importance in extracting useful diagnostic information from the structural attributes of tissues, which vary in response to the tissue health status and hence find great potential in cancer diagnosis. However, the complexities associated with cancer make it challenging to isolate the characteristic changes as the tumor progresses using polarimetry. This study attempts to experimentally characterize the polarimetric behavior in colon cancer associated with various stages of development. Bulk and unstained sections of normal and tumor colon tissue were imaged in the reflection and transmission polarimetry configurations at low and high imaging resolutions using an in-house developed Mueller polarimeter. Through this study, we observed that the information about the major contributors of scattering in colon tissue, manifesting in depolarization and retardance, can be obtained from the bulk tissue and unstained sections. These parameters aid in characterizing the polarimetric changes as the colon tumor progresses. While the unstained colon section best indicated the depolarization contrast between normal and tumor, the contrast through the retardance parameter was more pronounced in the bulk colon tissue. The results suggest that the polarimetric "digitally stained" images obtained by Mueller polarimetry are comparable with the bulk tissue counterparts, making it useful for characterizing colon cancer tissues across different stages of development.

Ultrasound-activated prodrug-loaded liposome for efficient cancer targeting therapy without chemotherapy-induced side effects.

BACKGROUND: Off-targeted distribution of chemotherapeutic drugs causes severe side effects, further leading to poor prognosis and patient compliance. Ligand/receptor-mediated targeted drug delivery can improve drug accumulation in the tumor but it always attenuated by protein corona barriers. RESULTS: To address these problems, a radically different strategy is proposed that can leave the off-targeted drugs inactive but activate the tumor-distributed drugs for cancer-targeting therapy in a tumor microenvironment-independent manner. The feasibility and effectiveness of this strategy is demonstrated by developing an ultrasound (US)-activated prodrug-loaded liposome (CPBSN38L) comprising the sonosensitizer chlorin e6 (Ce6)-modified lipids and the prodrug of pinacol boronic ester-conjugated SN38 (PBSN38). Once CPBSN38L is accumulated in the tumor and internalized into the cancer cells, under US irradiation, the sonosensitizer Ce6 rapidly induces extensive production of intracellular reactive oxygen species (ROS), thereby initiating a cascade amplified ROS-responsive activation of PBSN38 to release the active SN38 for inducing cell apoptosis. If some of the injected CPBSN38L is distributed into normal tissues, the inactive PBSN38 exerts no pharmacological activity on normal cells. CPBSN38L exhibited strong anticancer activity in multiple murine tumor models of colon adenocarcinoma and hepatocellular carcinoma with no chemotherapy-induced side effects, compared with the standard first-line anticancer drugs irinotecan and topotecan. CONCLUSIONS: This study established a side-effect-evitable, universal, and feasible strategy for cancer-targeting therapy.

The diagnostic accuracy of local staging in colon cancer based on computed tomography (CT): evaluating the role of extramural venous invasion and tumour deposits.

PURPOSE: The shift from adjuvant to neoadjuvant treatment in colon cancer demands the radiological selection of patients for systemic therapy. The aim of this study was to evaluate the accuracy of the CT-based TNM stage and high-risk features, including extramural venous invasion (EMVI) and tumour deposits, in the identification of patients with histopathological advanced disease, currently considered for neoadjuvant treatment (T3-4 disease). METHODS: All consecutive patients surgically treated for non-metastatic colon cancer between January 2018 and January 2020 in a referral centre for colorectal cancer were identified retrospectively. All tumours were staged on CT according to the TNM classification system. Additionally, the presence of EMVI and tumour deposits on CT was evaluated. The histopathological TNM classification was used as reference standard. RESULTS: A total of 176 patients were included. Histopathological T3-4 colon cancer was present in 85.0% of the patients with CT-detected T3-4 disease. Histopathological T3-4 colon cancer was present in 96.4% of the patients with CT-detected T3-4 colon cancer in the presence of both CT-detected EMVI and CT-detected tumour deposits. Histopathological T0-2 colon cancer was present in 50.8% of the patients with CT-detected T0-2 disease, and in 32.4% of the patients without CT-detected EMVI and tumour deposits. CONCLUSION: The diagnostic accuracy of CT-based staging was comparable with previous studies. The presence of high-risk features on CT increased the probability of histopathological T3-4 colon cancer. However, a substantial part of the patients without CT-detected EMVI and tumour deposits was diagnosed with histopathological T3-4 disease. Hence, more accurate selection criteria are required to correctly identify patients with locally advanced disease.

Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy.

BACKGROUND: In laparoscopic right hemicolectomy (RHC) for right-sided colon cancer, accurate recognition of the vascular anatomy is required for appropriate lymph node harvesting and safe operative procedures. We aimed to develop a deep learning model that enables the automatic recognition and visualization of major blood vessels in laparoscopic RHC. MATERIALS AND METHODS: This was a single-institution retrospective feasibility study. Semantic segmentation of three vessel areas, including the superior mesenteric vein (SMV), ileocolic artery (ICA), and ileocolic vein (ICV), was performed using the developed deep learning model. The Dice coefficient, recall, and precision were utilized as evaluation metrics to quantify the model performance after fivefold cross-validation. The model was further qualitatively appraised by 13 surgeons, based on a grading rubric to assess its potential for clinical application. RESULTS: In total, 2624 images were extracted from 104 laparoscopic colectomy for right-sided colon cancer videos, and the pixels corresponding to the SMV, ICA, and ICV were manually annotated and utilized as training data. SMV recognition was the most accurate, with all three evaluation metrics having values above 0.75, whereas the recognition accuracy of ICA and ICV ranged from 0.53 to 0.57 for the three evaluation metrics. Additionally, all 13 surgeons gave acceptable ratings for the possibility of clinical application in rubric-based quantitative evaluations. CONCLUSION: We developed a DL-based vessel segmentation model capable of achieving feasible identification and visualization of major blood vessels in association with RHC. This model may be used by surgeons to accomplish reliable navigation of vessel visualization.

Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer.

PURPOSE: To investigate whether texture analysis of primary colonic mass in preoperative abdominal computed tomography (CT) scans of patients diagnosed with colon cancer could predict tumor grade, T stage, and lymph node involvement using machine learning (ML) algorithms. MATERIALS AND METHODS: This retrospective study included 73 patients diagnosed with colon cancer. Texture features were extracted from contrast-enhanced CT images using LifeX software. First, feature reduction was performed by two radiologists through reproducibility analysis. Using the analysis of variance method, the parameters that best predicted lymph node involvement, grade, and T stage were determined. The predictive performance of these parameters was assessed using Orange software with the k-nearest neighbor (kNN), random forest, gradient boosting, and neural network models, and their area under the curve values were calculated. RESULTS: There was excellent reproducibility between the two radiologists in terms of 49 of the 58 texture parameters that were subsequently subject to further analysis. Considering all four ML algorithms, the mean AUC and accuracy ranges were 0.557-0.800 and 47-76%, respectively, for the prediction of lymph node involvement; 0.666-0.846 and 68-77%, respectively, for the prediction of grade; and 0.768-0.962 and 81-88%, respectively, for the prediction of T stage. The best performance was achieved with the random forest model in the prediction of LN involvement, the kNN model for the prediction of grade, and the gradient boosting model for the prediction of T stage. CONCLUSION: The results of this study suggest that the texture analysis of preoperative CT scans obtained for staging purposes in colon cancer can predict the presence of advanced-stage tumors, high tumor grade, and lymph node involvement with moderate specificity and sensitivity rates when evaluated using ML models.

Small arteriole sign: an imaging feature for staging T4a colon cancer.

OBJECTIVES: By analyzing the distribution of existing and newly proposed staging imaging features in pT1-3 and pT4a tumors, we searched for a salient feature and validated its diagnostic performance. METHODS: Preoperative multiphase contrast-enhanced CT images of the training cohort were retrospectively collected at three centers from January 2016 to December 2017. We used the chi-square test to analyze the distribution of several stage-related imaging features in pT1-3 and pT4a tumors, including small arteriole sign (SAS), outer edge of the intestine, tumor invasion range, and peritumoral adipose tissue. Preoperative multiphase contrast-enhanced CT images of the validation cohort were retrospectively collected at Beijing Cancer Hospital from January 2018 to December 2018. The diagnostic performance of the selected imaging feature, including accuracy, sensitivity, and specificity, was validated and compared with the conventional clinical tumor stage (cT) by the McNemar test. RESULTS: In the training cohort, a total of 268 patients were enrolled, and only SAS was significantly different between pT1-3 and pT4a tumors. The accuracy, sensitivity, and specificity of the SAS and conventional cT in differentiating T1-3 and T4a tumors were 94.4%, 81.6%, and 97.3% and 53.7%, 32.7%, and 58.4%, respectively (all p < 0.001). In the validation cohort, a total of 135 patients were collected. The accuracy, sensitivity, and specificity of the SAS and the conventional cT were 93.3%, 76.2%, and 96.5% and 62.2%, 38.1%, and 66.7%, respectively (p < 0.001, p = 0.021, p < 0.001). CONCLUSION: Small arteriole sign positivity, an indirect imaging feature of serosa invasion, may improve the accuracy of identifying T4a colon cancer. CLINICAL RELEVANCE STATEMENT: Small arteriole sign helps to distinguish T1-3 and T4a colon cancer and further improves the accuracy of preoperative CT staging of colon cancer. KEY POINTS: • The accuracy of preoperative CT staging of colon cancer is not ideal, especially for T4a tumors. • Small arteriole sign (SAS) is a newly defined imaging feature that shows the appearance of tumor-supplying arterioles at the site where they penetrate the intestine wall. • SAS is an indirect imaging marker of tumor invasion into the serosa with a great value in distinguishing between T1-3 and T4a colon cancer.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT. METHODS: In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC. RESULTS: A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively. CONCLUSION: A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

A feature engineering-based machine learning technique to detect and classify lung and colon cancer from histopathological images.

Globally, lung and colon cancers are among the most prevalent and lethal tumors. Early cancer identification is essential to increase the likelihood of survival. Histopathological images are considered an appropriate tool for diagnosing cancer, which is tedious and error-prone if done manually. Recently, machine learning methods based on feature engineering have gained prominence in automatic histopathological image classification. Furthermore, these methods are more interpretable than deep learning, which operates in a "black box" manner. In the medical profession, the interpretability of a technique is critical to gaining the trust of end users to adopt it. In view of the above, this work aims to create an accurate and interpretable machine-learning technique for the automated classification of lung and colon cancers from histopathology images. In the proposed approach, following the preprocessing steps, texture and color features are retrieved by utilizing the Haralick and Color histogram feature extraction algorithms, respectively. The obtained features are concatenated to form a single feature set. The three feature sets (texture, color, and combined features) are passed into the Light Gradient Boosting Machine (LightGBM) classifier for classification. And their performance is evaluated on the LC25000 dataset using hold-out and stratified 10-fold cross-validation (Stratified 10-FCV) techniques. With a test/hold-out set, the LightGBM with texture, color, and combined features classifies the lung and colon cancer images with 97.72%, 99.92%, and 100% accuracy respectively. In addition, a stratified 10-fold cross-validation method also revealed that LightGBM's combined or color features performed well, with an excellent mean auc_mu score and a low mean multi_logloss value. Thus, this proposed technique can help histologists detect and classify lung and colon histopathology images more efficiently, effectively, and economically, resulting in more productivity.

Hafnium (Hf)-Chelating Porphyrin-Decorated Gold Nanosensitizers for Enhanced Radio-Radiodynamic Therapy of Colon Carcinoma.

X-ray-induced radiodynamic therapy (RDT) that can significantly reduce radiation dose with an improved anticancer effect has emerged as an attractive and promising therapeutic modality for tumors. However, it is highly significant to develop safe and efficient radiosensitizing agents for tumor radiation therapy. Herein, we present a smart nanotheranostic system FA-Au-CH that consists of gold nanoradiosensitizers, photosensitizer chlorin e6 (Ce6), and folic acid (FA) as a folate-receptor-targeting ligand for improved tumor specificity. FA-Au-CH nanoparticles have been demonstrated to be able to simultaneously serve as radiosensitizers and RDT agents for enhanced computed tomography (CT) imaging-guided radiotherapy (RT) of colon carcinoma, owing to the strong X-ray attenuation capability of high-Z elements Au and Hf, as well as the characteristics of Hf that can transfer radiation energy to Ce6 to generate ROS from Ce6 under X-ray irradiation. The integration of RT and RDT in this study demonstrates great efficacy and offers a promising therapeutic modality for the treatment of malignant tumors.

Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer.

The goal of this study is to develop a mathematical model that captures the interaction between evofosfamide, immunotherapy, and the hypoxic landscape of the tumor in the treatment of tumors. Recently, we showed that evofosfamide, a hypoxia-activated prodrug, can synergistically improve treatment outcomes when combined with immunotherapy, while evofosfamide alone showed no effects in an in vivo syngeneic model of colorectal cancer. However, the mechanisms behind the interaction between the tumor microenvironment in the context of oxygenation (hypoxic, normoxic), immunotherapy, and tumor cells are not fully understood. To begin to understand this issue, we develop a system of ordinary differential equations to simulate the growth and decline of tumors and their vascularization (oxygenation) in response to treatment with evofosfamide and immunotherapy (6 combinations of scenarios). The model is calibrated to data from in vivo experiments on mice implanted with colon adenocarcinoma cells and longitudinally imaged with [18F]-fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) to quantify hypoxia. The results show that evofosfamide is able to rescue the immune response and sensitize hypoxic tumors to immunotherapy. In the hypoxic scenario, evofosfamide reduces tumor burden by $ 45.07 \pm 2.55 $%, compared to immunotherapy alone, as measured by tumor volume. The model accurately predicts the temporal evolution of five different treatment scenarios, including control, hypoxic tumors that received immunotherapy, normoxic tumors that received immunotherapy, evofosfamide alone, and hypoxic tumors that received combination immunotherapy and evofosfamide. The average concordance correlation coefficient (CCC) between predicted and observed tumor volume is $ 0.86 \pm 0.05 $. Interestingly, the model values to fit those five treatment arms was unable to accurately predict the response of normoxic tumors to combination evofosfamide and immunotherapy (CCC = $ -0.064 \pm 0.003 $). However, guided by the sensitivity analysis to rank the most influential parameters on the tumor volume, we found that increasing the tumor death rate due to immunotherapy by a factor of $ 18.6 \pm 9.3 $ increases CCC of $ 0.981 \pm 0.001 $. To the best of our knowledge, this is the first study to mathematically predict and describe the increased efficacy of immunotherapy following evofosfamide.

TransResU-Net: A Transformer based ResU-Net for Real-Time Colon Polyp Segmentation.

Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early detection. Colonoscopy is the primary modality used to diagnose colon cancer. However, the miss rate of polyps, adenomas and advanced adenomas remains significantly high. Early detection of polyps at the precancerous stage can help reduce the mortality rate and the economic burden associated with colorectal cancer. Deep learning-based computer-aided diagnosis (CADx) system may help gastroenterologists to identify polyps that may otherwise be missed, thereby improving the polyp detection rate. Additionally, CADx system could prove to be a cost-effective system that improves long-term colorectal cancer prevention. In this study, we proposed a deep learning-based architecture for automatic polyp segmentation called Transformer ResU-Net (TransResU-Net). Our proposed architecture is built upon residual blocks with ResNet-50 as the backbone and takes advantage of the transformer self-attention mechanism as well as dilated convolution(s). Our experimental results on two publicly available polyp segmentation benchmark datasets showed that TransResU-Net obtained a highly promising dice score and a real-time speed. With high efficacy in our performance metrics, we concluded that TransResU-Net could be a strong benchmark for building a real-time polyp detection system for the early diagnosis, treatment, and prevention of colorectal cancer. The source code of the proposed TransResU-Net is publicly available at https://github.com/nikhilroxtomar/TransResUNet.

Usefulness of intraoperative ultrasound examination for laparoscopic right-side colon cancer surgery: a propensity score-matched study.

Complete mesocolic excision (CME) with central vascular ligation (CVL) in laparoscopic surgery for right-sided colon cancer (RSCC) requires a precise understanding of the vascular anatomy. The efficacy of intraoperative ultrasound (IUS) in the identification of blood vessels for RSCC surgery was not evaluated. The aim of this study was to compare the intraoperative and short-term outcomes of CME with CVL with or without IUS by laparoscopic surgery for RSCC. We performed IUS on 26 patients of RSCC and compared with a total of 124 patients who underwent the surgery for RSCC at our institution. Propensity score matching (PSM) was performed to reduce the confounding effects to imbalances in the use of IUS. The IUS identified the main feeding artery and the accompanying vein in all 26 cases. After PSM, the amount of intraoperative blood loss in the IUS group was significantly lower than that in the conventional group (5 ml vs. 30 ml, p = 0.035) and no significant difference of the postoperative complications was observed. The IUS reduced the risk of bleeding in the surgery for RSCC. The IUS is a safe and feasible technique that help the surgeons for anatomical understandings under real-time condition in the laparoscopic surgery of RSCC.

Two pathological fractures due to mandibular metastasis, rare in colon cancer; a case report presentation.

We reported on 65 years old patient who has colon cancer and referred to our palliative care center with pain due to enlarging metastatic mass on the dorsal of the right hand. She had swelling and numbness on her jaw. Computed tomography (CT) scan was performed for mandible imaging and two pathologic fractures were detected on the right corpus and right condyle of the mandible. Clinicians should consider possible metastases for terminal stage cancer patients.