Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

BACKGROUND: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS: We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage. RESULTS: Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels. CONCLUSIONS: Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.

The impact and opportunities of large language models like ChatGPT in oral and maxillofacial surgery: a narrative review.

Since its release at the end of 2022, the social response to ChatGPT, a large language model (LLM), has been huge, as it has revolutionized the way we communicate with computers. This review was performed to describe the technical background of LLMs and to provide a review of the current literature on LLMs in the field of oral and maxillofacial surgery (OMS). The PubMed, Scopus, and Web of Science databases were searched for LLMs and OMS. Adjacent surgical disciplines were included to cover the entire literature, and records from Google Scholar and medRxiv were added. Out of the 57 records identified, 37 were included; 31 (84%) were related to GPT-3.5, four (11%) to GPT-4, and two (5%) to both. Current research on LLMs is mainly limited to research and scientific writing, patient information/communication, and medical education. Classic OMS diseases are underrepresented. The current literature related to LLMs in OMS has a limited evidence level. There is a need to investigate the use of LLMs scientifically and systematically in the core areas of OMS. Although LLMs are likely to add value outside the operating room, the use of LLMs raises ethical and medical regulatory issues that must first be addressed.

Multimodal survival prediction in advanced pancreatic cancer using machine learning.

BACKGROUND: Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care. METHODS: In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups. RESULTS: Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59). CONCLUSIONS: The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression.

BACKGROUND AND PURPOSE: MR imaging in neuro-oncology is challenging due to inherent ambiguities in proton signal behavior. Sodium-MR imaging may substantially contribute to the characterization of tumors because it reflects the functional status of the sodium-potassium pump and sodium channels. MATERIALS AND METHODS: Sodium-MR imaging data of patients with treatment-naïve glioma WHO grades I-IV (n = 34; mean age, 51.29 ± 17.77 years) were acquired by using a 7T MR system. For acquisition of sodium-MR images, we applied density-adapted 3D radial projection reconstruction pulse sequences. Proton-MR imaging data were acquired by using a 3T whole-body system. RESULTS: We demonstrated that the initial sodium signal of a treatment-naïve brain tumor is a significant predictor of isocitrate dehydrogenase (IDH) mutation status (P < .001). Moreover, independent of this correlation, the Cox proportional hazards model confirmed the sodium signal of treatment-naïve brain tumors as a predictor of progression (P = .003). Compared with the molecular signature of IDH mutation status, information criteria of model comparison revealed that the sodium signal is even superior to IDH in progression prediction. In addition, sodium-MR imaging provides a new approach to noninvasive tumor classification. The sodium signal of contrast-enhancing tumor portions facilitates differentiation among most glioma types (P < .001). CONCLUSIONS: The information of sodium-MR imaging may help to classify neoplasias at an early stage, to reduce invasive tissue characterization such as stereotactic biopsy specimens, and overall to promote improved and individualized patient management in neuro-oncology by novel imaging signatures of brain tumors.

A transgenic approach to identify thyroxine transporter-expressing structures in brain development.

Transporters are essential in thyroid hormone metabolism. Thyroxine (T4) is transported by solute carrier organic anion transporter 1c1 (SLCO1C1, OATP14) into the adult brain, where T4 is converted to 3,5,3'-triiodothyronine (T3). In adults, SLCO1C1 expression is found in two brain barrier structures: the blood-brain barrier (BBB) and choroid plexus. However, little is known about how T4 is transported in the developing brain, when the BBB is not yet completely formed. We employed bacterial artificial chromosome recombineering to generate transgenic mice carrying Cre recombinase in the Slco1c1 locus (Slco1c1-Cre mice). In Slco1c1-Cre mice Cre was expressed at the sites that have been previously reported for SLCO1C1 in adults. To trace Cre expression during development, we crossed Slco1c1-Cre transgenic mice with Rosa26 reporter mice. ß-galactosidase staining showed Cre activity in neurones of various brain structures, such as cortical layer 2/3 and the hippocampus, suggesting transient Slco1c1 expression during brain development. At embryonic day15, SLCO1C1 was expressed at the same site as TBR2, a marker of neuronal progenitors. Neurones that express SLCO1C1 during their development could be T4 sensitive. In support of this hypothesis, hypothyroxinaemia induced by propylthiouracil treatment of dams decreased the number of ß-galactosidase-positive neurones in cortical layer 2/3 of newborn Slco1c1-Cre/Rosa26 mice. In conclusion, by generating Slco1c1-Cre transgenic mice, we demonstrated that SLCO1C1 is expressed in the neuronal cell lineage during brain development. Expression of SLCO1C1 may underlie the extraordinary sensitivity of specific neuronal populations to hypothyroxinaemia.