Impact of body composition and physical strength changes during chemoradiotherapy on complications and survival after oesophagectomy.

BACKGROUND: The aim of this study was to assess body composition and physical strength changes during neoadjuvant chemoradiotherapy (nCRT) and assess their predictive value for (severe) postoperative complications and overall survival in patients who underwent oesophagectomy for oesophageal cancer. METHODS: Consecutive patients who underwent nCRT and oesophagectomy with curative intent in a tertiary referral center were included in the study. Perioperative data were collected in a prospectively maintained database. The CT images before and after nCRT were used to assess skeletal muscle index (SMI), subcutaneous fat index (SFI), and visceral fat index (VFI). To assess physical strength, handgrip strength (HGS) and the exercise capacity of the steep ramp test (SRT Wpeak) were acquired before and after nCRT. RESULTS: Between 2015 and 2020, 126 patients were included. SMI increased in female subgroups and decreased in male subgroups (35.38 to35.60 cm2/m2 for females, P value 0.048, 46.89 to 45.34 cm2/m2 for males, P value < 0.001). No significant changes in SFI, VFI, HGS, and SRT Wpeak were observed. No predictive value of changes in SMI, HGS, and SRT Wpeak was shown for (severe) postoperative complications and overall survival. CONCLUSIONS: A significant but minimal decrease in SMI during nCRT was observed for males only, it was not associated with postoperative complications or overall survival. Physical strength measurements did not decrease significantly over the course of nCRT. No associations with postoperative complications or overall survival were observed.

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy.

OBJECTIVE: To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. BACKGROUND: RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. METHODS: Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. RESULTS: The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. CONCLUSION: This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.

Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review.

BACKGROUND: Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery. METHODS: This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time. RESULTS: After full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation. CONCLUSIONS: The accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research. TRIAL REGISTRATION: PROSPERO registration number: CRD42021264226.

[A 51-year-old woman with upper abdominal pain]. / Een 51-jarige vrouw met pijn in de bovenbuik.

A 51-year-old woman presented to the emergency room with upper abdominal pain and elevated infection parameters. No abnormalities were found during gastroscopy. A CT scan demonstrated perigastricappendagitis. Perigastricappendagitis is a rare infarction of a fatty appendix of the perigastric ligaments. It is a benign and self-limiting disease.

Impact on postoperative complications of changes in skeletal muscle mass during neoadjuvant chemotherapy for gastro-oesophageal cancer.

BACKGROUND: Risk assessment is relevant to predict postoperative outcomes in patients with gastro-oesophageal cancer. This cohort study aimed to assess body composition changes during neoadjuvant chemotherapy and investigate their association with postoperative complications. METHODS: Consecutive patients with gastro-oesophageal cancer undergoing neoadjuvant chemotherapy and surgery with curative intent between 2016 and 2019 were identified from a specific database and included in the study. CT images before and after neoadjuvant chemotherapy were used to assess the skeletal muscle index, sarcopenia, and subcutaneous and visceral fat index. RESULTS: In a cohort of 199 patients, the mean skeletal muscle index decreased during neoadjuvant therapy (from 51·187 to 49·19 cm2 /m2 ; P < 0·001) and the rate of sarcopenia increased (from 42·2 to 54·3 per cent; P < 0·001). A skeletal muscle index decrease greater than 5 per cent was not associated with an increased risk of total postoperative complications (odds ratio 0·91, 95 per cent c.i. 0·52 to 1·59; P = 0·736) or severe complications (odds ratio 0·66, 0·29 to 1·53; P = 0·329). CONCLUSION: Skeletal muscle index decreased during neoadjuvant therapy but was not associated with postoperative complications.

ANTECEDENTES: La evaluación de riesgo es importante para predecir los resultados postoperatorios en pacientes con cáncer gastroesofágico. Este estudio de cohortes tuvo como objetivo evaluar los cambios en la composición corporal durante la quimioterapia neoadyuvante e investigar su asociación con complicaciones postoperatorias. MÉTODOS: Los pacientes consecutivos con cáncer gastroesofágico sometidos a quimioterapia neoadyuvante y cirugía con intención curativa entre 2016 y 2019, identificados a partir de una base de datos específica, se incluyeron en el estudio. Se utilizaron las imágenes de tomografía computarizada, antes y después de la quimioterapia neoadyuvante, para evaluar el índice de masa muscular esquelética, la sarcopenia y el índice de grasa visceral y subcutánea. RESULTADOS: En una cohorte de 199 pacientes, el índice de masa muscular esquelética disminuyó durante el tratamiento neoadyuvante (de 51,87 cm2 /m2 a 49,19 cm2 /m2 , P < 0,001) y las tasas de sarcopenia aumentaron (de 42,2% a 54,2%, P < 0,001). Una disminución del índice de masa muscular esquelética > 5% no se asoció con un mayor riesgo de complicaciones postoperatorias globales (razón de oportunidades, odds ratio: 0,908; ic. del 95%: 0,520-1,587, P = 0,736) ni de complicaciones graves (odds ratio: 0,661; i.c. del 95%: 0,286-1,525, P = 0,329). CONCLUSIÓN: El índice de masa muscular esquelética disminuyó durante el tratamiento neoadyuvante, pero no se asoció con complicaciones postoperatorias.