Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Comparison of two different types of hybrid Tibial fixations for anterior cruciate ligament reconstruction: a prospective comparative cohort study.

BACKGROUND: Previous studies have compared different kinds of fixations for anterior cruciate ligament reconstruction. Nevertheless, there is no optimal method to date. To the best of authors' knowledge, there is no article discussing the combination of adjustable suspensory device and interference screw for hybrid tibial fixation. METHODS: In total, 66 patients (n = 34, adjustable suspensory device and interference screw; n = 32, cortical screw and interference screw) were analyzed. Their International Knee Documentation Committee score and Tegner activity level scale were evaluated before and after a 2-year follow-up. The Single Assessment Numeric Evaluation score was evaluated after a 2-year follow-up. Physical exams such as range of motion, anterior knee pain (VAS > = 3) and Lachman test were assessed before and at least 12 months after surgery. To evaluate tunnel widening, anteroposterior and lateral view radiography was conducted 1 day and at least 12 months after surgery. A more than 10% change was considered tibial tunnel widening. Mann-Whitney U test, independent t test, paired t test, Fisher's exact test and chi-squared test were used to compare the variables. Linear and logistic regression models were applied to adjust for potential confounders. RESULTS: No variable except gender (P = 0.006) showed significant difference with regard to demographic data. After adjustment, there was no statistically significant difference between the groups regarding post-operative physical exams. Patients who used adjustable suspensory device and interference screw had lower post-operative Single Assessment Numeric Evaluation score (adjusted ß - 8.194; P = 0.017), Tegner activity level scale (adjusted ß - 1.295; P = 0.001) and pre-operative degrees of knee flexion (adjusted ß - 2.825; P = 0.026). Less percentage of tunnel widening in the lateral view of radiographs was seen in patients in group of adjustable suspensory device and interference screw (adjusted ß - 1.733; P = 0.038). No significant difference was observed in the anteroposterior view of radiographs (adjusted ß - 0.667; P = 0.26). CONCLUSION: In these 66 patients, we observed less tibial tunnel widening and lower post-operative functional scores in the group of adjustable suspensory device and interference screw. Both groups displayed similar outcomes of physical exams as well as improvement after operation. The proposed method may become an alternative option. Nonetheless, the quality of our study is still limited, and thus further studies are warranted to determine the efficacy and further application. TRIAL REGISTRATION: Joint Institutional Review Board of Taipei Medical University, Taipei, Taiwan (No: N201805094 ). STUDY DESIGN: Prospective comparative cohort study; Level of evidence, II.

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer.

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher's exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Chemoenzymatic Synthesis of GAA-7 Glycan Analogues and Evaluation of Their Neuritogenic Activities.

Ganglioside GAA-7 exhibits higher neurite outgrowth than ganglioside GM1a and most echinodermatous gangliosides (EGs) when tested on neuron-like rat adrenal pheochromocytoma (PC12) cells in the presence of nerve growth factor (NGF). The unique structure of GAA-7 glycan, containing an uncommon sialic acid (8-O-methyl-N-glycolylneuraminic acid) and sialic acid-α-2,3-GalNAc linkage, makes it challenging to synthesize. We recently developed a streamlined method to chemoenzymatically synthesize GAA-7 glycan and employed this modular strategy to efficiently prepare a library of GAA-7 glycan analogues incorporating N-modified or 8-methoxyl sialic acids. Most of these synthetic glycans exhibited moderate efficacy in promoting neuronal differentiation of PC12 cells. Among them, the analogue containing common sialic acid shows greater potential than the GAA-7 glycan itself. This result reveals that methoxy modification is not essential for neurite outgrowth. Consequently, the readily available analogue presents a promising model for further biological investigations.

Deep learning to assess microsatellite instability directly from histopathological whole slide images in endometrial cancer.

Molecular classification, particularly microsatellite instability-high (MSI-H), has gained attention for immunotherapy in endometrial cancer (EC). MSI-H is associated with DNA mismatch repair defects and is a crucial treatment predictor. The NCCN guidelines recommend pembrolizumab and nivolumab for advanced or recurrent MSI-H/mismatch repair deficient (dMMR) EC. However, evaluating MSI in all cases is impractical due to time and cost constraints. To overcome this challenge, we present an effective and efficient deep learning-based model designed to accurately and rapidly assess MSI status of EC using H&E-stained whole slide images. Our framework was evaluated on a comprehensive dataset of gigapixel histopathology images of 529 patients from the Cancer Genome Atlas (TCGA). The experimental results have shown that the proposed method achieved excellent performances in assessing MSI status, obtaining remarkably high results with 96%, 94%, 93% and 100% for endometrioid carcinoma G1G2, respectively, and 87%, 84%, 81% and 94% for endometrioid carcinoma G3, in terms of F-measure, accuracy, precision and sensitivity, respectively. Furthermore, the proposed deep learning framework outperforms four state-of-the-art benchmarked methods by a significant margin (p < 0.001) in terms of accuracy, precision, sensitivity and F-measure, respectively. Additionally, a run time analysis demonstrates that the proposed method achieves excellent quantitative results with high efficiency in AI inference time (1.03 seconds per slide), making the proposed framework viable for practical clinical usage. These results highlight the efficacy and efficiency of the proposed model to assess MSI status of EC directly from histopathological slides.