Large language models for oncological applications.

Background Various predictive scoring systems have been developed to estimate outcomes of patients undergoing surgery for colorectal liver metastases (CRLM). However, data regarding their effectiveness in recurrent CRLM (recCRLM) are very limited. Methods Patients who underwent repeat hepatectomy for recCRLM at the University Hospital RWTH Aachen, Germany from 2010-2021 were included. Nine predictive scoring systems (Fong’s, Nordlinger, Nagashima, RAS mutation, Tumor Burden, GAME, CERR, and Glasgow Prognostic score, Basingstoke Index) were evaluated by likelihood ratio (LR) χ2, linear trend (LT) χ2 and Akaike Information Criterion (AIC) for their predictive value regarding overall survival (OS) and recurrence free survival (RFS). Results Among 150 patients, median RFS was 9 (2-124) months with a 5-year RFS rate of 10%. Median OS was 39 (4-131) months with a 5-year OS rate of 32%. For RFS and OS, the Nagashima score showed the best prognostic ability (LT χ2 3.00, LR χ2 9.39, AIC 266.66 and LT χ2 2.91, LR χ2 20.91, 290.36). Discussion The Nagashima score showed the best prognostic stratification to predict recurrence as well as survival, and therefore might be considered when evaluating patients with recCRLM for repeat hepatectomy. 

Value of prognostic scoring systems in the era of multimodal therapy for recurrent colorectal liver metastases.

in the Genetic and Morphological Evaluation (GAME) and Comprehensive Evaluation of Relapse Risk (CERR) scores,

Outcome prediction after resection of colorectal cancer liver metastases: out with the old, in with the new?

Identifying the precise topography of cancer for targeted biopsy in colonoscopic examination is a challenge in current diagnostic practice. For the first time we demonstrate the use of compression optical coherence elastography (C-OCE) technology as a new functional OCT modality for differentiating between cancerous and non-cancerous tissues in colon and detecting their morphological features on the basis of measurement of tissue elastic properties. The method uses pre-determined stiffness values (Young’s modulus) to distinguish between different morphological structures of normal (mucosa and submucosa), benign tumor (adenoma) and malignant tumor tissue (including cancer cells, gland-like structures, cribriform gland-like structures, stromal fibers, extracellular mucin). After analyzing in excess of fifty tissue samples, a threshold stiffness value of 520 kPa was suggested above which areas of colorectal cancer were detected invariably. A high Pearson correlation (r =0.98; p <0.05), and a negligible bias (0.22) by good agreement of the segmentation results of C-OCE and histological (reference standard) images was demonstrated, indicating the efficiency of C-OCE to identify the precise localization of colorectal cancer and the possibility to perform targeted biopsy. Furthermore, we demonstrated the ability of C-OCE to differentiate morphological subtypes of colorectal cancer – low-grade and high-grade colorectal adenocarcinomas, mucinous adenocarcinoma, and cribriform patterns. The obtained ex vivo results highlight prospects of C-OCE for high-level colon malignancy detection. The future endoscopic use of C-OCE will allow targeted biopsy sampling and simultaneous rapid analysis of the heterogeneous morphology of colon tumors.

/pdf/towards-targeted-colorectal-cancer-biopsy-based-on-tissue-2ju5p5bx.pdf

Towards targeted colorectal cancer biopsy based on tissue morphology assessment by compression optical coherence elastography

Abstract Purpose Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo. Methods Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 ( n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process. Results Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively. Conclusion Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning. 

/pdf/optical-coherence-tomography-combined-with-convolutional-3dt3c6l0.pdf

Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo.

Abstract Purpose Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN). Methods Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out. Results A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively. Conclusion Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins. 

https://link.springer.com/content/pdf/10.1007/s00432-022-04263-z.pdf

Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo

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Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo

Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo.