Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients.

INTRODUCTION: This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients. METHODS: A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012-2019 at a single Australian tertiary institution, Western Health in Melbourne. A two-dimensional U-Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross-sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods. RESULTS: The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients >0.98, respectively) and test (Dice similarity coefficients >0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944-0.999, P < 0.001). CONCLUSIONS: Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.

Digital health and medical technology for perioperative sarcopenia optimisation and prehabilitation - Where to go from here?

Sarcopenia has been shown to cause poorer outcomes in surgical oncology. With the advancement of artificial intelligence technology, body composition analysis can be obtained with ease. Given the lead time between cancer diagnosis and surgery, selected patients at risk of complications secondary to sarcopenia may be amenable to prehabilitation. The COVID-19 pandemic has led to the rapid adaptation of digital health and medical technology. Our perspective piece will review the current available evidence and propose innovative ways to incorporate technology into physical and nutritional prehabilitation.

Review article: Telehealth in Emergency Medicine in Australasia: Advantages and barriers.

The COVID-19 pandemic catapulted Telehealth to the forefront of Emergency Medicine (EM) as an alternative way of assessing and managing patients. This challenged the traditional idea that EM can only be practised within brick-and-mortar EDs. Many Emergency Physicians may find the idea of practising Telehealth in Emergency Medicine (TEM) confronting, particularly in the absence of training and clear practice guidelines. The purpose of the present paper is to describe the current use of TEM in Australasia, and outline the advantages and barriers in adopting this practice domain.

Two cases of atraumatic adrenal hemorrhage: A review of active management, conservative management, and challenges faced.

Adrenal hemorrhage (AH) is an uncommon and potentially disastrous affliction that carries an accepted mortality risk of 15%. Variable symptomatology can cause a diagnostic dilemma and may be missed. We present 2 cases of right-sided AH; both cases were initially presumed to be renal colic. Case 1 was an 86-year-old gentleman, presenting with right flank pain found to have a right-sided atraumatic AH. He presented with hemorrhagic shock, requiring angioembolization of the bleeding vessel. Case 2 was a 62-year-old gentleman who presented with right flank pain and was found to have a right-sided atraumatic AH. He was hemodynamically stable and successfully managed conservatively. Adrenal hemorrhage is a potentially fatal affliction that may be missed. CT scans are the recommended imaging modality during an acute presentation due to wider availability and fast assessment. We demonstrate a hemodynamically stable patient managed with a 'watch and wait' approach and an unstable patient managed with resuscitation followed by urgent angioembolization.

Body composition assessment by artificial intelligence can be a predictive tool for short-term postoperative complications in Hartmann's reversals.

BACKGROUND: Hartmann's reversal, a complex elective surgery, reverses and closes the colostomy in individuals who previously underwent a Hartmann's procedure due to colonic pathology like cancer or diverticulitis. It demands careful planning and patient optimisation to help reduce postoperative complications. Preoperative evaluation of body composition has been useful in identifying patients at high risk of short-term postoperative outcomes following colorectal cancer surgery. We sought to explore the use of our in-house derived Artificial Intelligence (AI) algorithm to measure body composition within patients undergoing Hartmann's reversal procedure in the prediction of short-term postoperative complications. METHODS: A retrospective study of all patients who underwent Hartmann's reversal within a single tertiary referral centre (Western) in Melbourne, Australia and who had a preoperative Computerised Tomography (CT) scan performed. Body composition was measured using our previously validated AI algorithm for body segmentation developed by the Department of Surgery, Western Precinct, University of Melbourne. Sarcopenia in our study was defined as a skeletal muscle index (SMI), calculated as Skeletal Muscle Area (SMA) /height2 < 38.5 cm2/m2 in women and < 52.4 cm2/m2 in men. RESULTS: Between 2010 and 2020, 47 patients (mean age 63.1 ± 12.3 years; male, n = 28 (59.6%) underwent body composition analysis. Twenty-one patients (44.7%) were sarcopenic, and 12 (25.5%) had evidence of sarcopenic obesity. The most common postoperative complication was surgical site infection (SSI) (n = 8, 17%). Sarcopenia (n = 7, 87.5%, p = 0.02) and sarcopenic obesity (n = 5, 62.5%, p = 0.02) were significantly associated with SSIs. The risks of developing an SSI were 8.7 times greater when sarcopenia was present. CONCLUSION: Sarcopenia and sarcopenic obesity were related to postoperative complications following Hartmann's reversal. Body composition measured by a validated AI algorithm may be a beneficial tool for predicting short-term surgical outcomes for these patients.

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

BACKGROUND: The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized treatment. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pCR in patients with LARC. METHODS: LARC patients who underwent NAT followed by surgery from 2012 to 2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in-house pre-trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar-3 vertebral level to produce a volumetric measurement of visceral fat (VF), subcutaneous fat (SCF) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed. RESULTS: Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (P = 0.015) and age (P = 0.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (P = 0.059). CONCLUSION: This is the first study in the literature utilizing AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.

Colorectal cancer treatment outcomes during the pandemic: Our experience of COVID-19 at a tertiary referral center.

BACKGROUNDS: The coronavirus disease 2019 (COVID-19) has led to major shifts in the management of colorectal cancer (CRC). This study aims to identify the impact and early outcomes of COVID-19 following CRC management at a tertiary referral center in Victoria, Australia. METHODS: This was a retrospective study, utilizing the Australian Comprehensive Cancer Outcomes and Research Database and inpatient records. Patients presenting for CRC management at our institution were identified coinciding with the first Victorian outbreak of COVID-19 (March 26 to September 26, 2020) (COVID). Management decisions including chemoradiotherapy utilization and surgical outcomes were analyzed within 6 months and compared with the corresponding period in 2019 (pre-COVID). RESULTS: A total of 276 patients were included in this study (147 pre-COVID period, 129 COVID period). During the COVID period, more patients (47.6% vs. 60.5%; p = 0.033) presented symptomatically and less for surveillance (10.9% vs. 2.3%; p < 0.01). Eighty-four pre-COVID and 69 COVID period patients proceeded to surgery. The average time from diagnosis date to surgery was 15.6 days less during the COVID period. There were no significant differences in postoperative utilization of higher care (p = 0.74), complications (p = 0.93), median hospital length of stay (p = 0.67), 30-day readmission (p = 0.50), or 30-day reoperation (p = 0.74). In 1.6% of cases, pandemic impacts resulted in a change in management. CONCLUSION: Presentation of patients with CRC varied, with a significant increase in symptomatic presentations and decreased numbers for surveillance. Through flexibility and change in practice, our institution helped improve access to surgical intervention and oncological therapies. Further prospective work is required to identify long-term outcomes and characterize the effects of ongoing disruptions.

Generative Large Language Models for Detection of Speech Recognition Errors in Radiology Reports.

This study evaluated the ability of generative large language models (LLMs) to detect speech recognition errors in radiology reports. A dataset of 3233 CT and MRI reports was assessed by radiologists for speech recognition errors. Errors were categorized as clinically significant or not clinically significant. Performances of five generative LLMs-GPT-3.5-turbo, GPT-4, text-davinci-003, Llama-v2-70B-chat, and Bard-were compared in detecting these errors, using manual error detection as the reference standard. Prompt engineering was used to optimize model performance. GPT-4 demonstrated high accuracy in detecting clinically significant errors (precision, 76.9%; recall, 100%; F1 score, 86.9%) and not clinically significant errors (precision, 93.9%; recall, 94.7%; F1 score, 94.3%). Text-davinci-003 achieved F1 scores of 72% and 46.6% for clinically significant and not clinically significant errors, respectively. GPT-3.5-turbo obtained 59.1% and 32.2% F1 scores, while Llama-v2-70B-chat scored 72.8% and 47.7%. Bard showed the lowest accuracy, with F1 scores of 47.5% and 20.9%. GPT-4 effectively identified challenging errors of nonsense phrases and internally inconsistent statements. Longer reports, resident dictation, and overnight shifts were associated with higher error rates. In conclusion, advanced generative LLMs show potential for automatic detection of speech recognition errors in radiology reports. Keywords: CT, Large Language Model, Machine Learning, MRI, Natural Language Processing, Radiology Reports, Speech, Unsupervised Learning Supplemental material is available for this article.

Adjuvant Chemotherapy for Older Patients With Stage III Colorectal Cancer: A Real-World Analysis of Treatment Recommendations, Treatment Administered and Impact on Cancer Recurrence.

BACKGROUND: A substantial proportion of patients with stage III colorectal cancer (CRC) are older than 70 years. Optimal adjuvant chemotherapy (AC) for older patients (OP) continues to be debated, with subgroup analyses of randomized trials not demonstrating a survival benefit from the addition of oxaliplatin to a fluoropyrimidine backbone. PATIENTS AND METHODS: We analyzed the multisite Australian ACCORD registry, which prospectively collects patient, tumor and treatment data along with long term clinical follow-up. We compared OP (≥70) with stage III CRC to younger patients ([YP] <70), including the proportion recommended AC and any reasons for not prescribing AC. AC administration, regimen choice, completion rates, and survival outcomes were also examined. RESULTS: One thousand five hundred twelve patients enrolled in the ACCORD registry from 2005 to 2018 were included. Median follow-up was 57.0 months. Compared to the 827 YP, the 685 OP were less likely to be offered AC (71.5% vs. 96.5%, P < .0001) and when offered, were more likely to decline treatment (15.1% vs. 2.8%, P < .0001). Ultimately, 60.0% of OP and 93.7% of YP received AC (P < .0001). OP were less likely to receive oxaliplatin (27.5% vs. 84.7%, P < .0001) and to complete AC (75.9% vs. 85.7%, P < .0001). The probability of remaining recurrence-free was significantly higher in OP who received AC compared to those not treated (HR 0.73, P = .04) but not significantly improved with the addition of oxaliplatin (HR 0.75, P = .18). CONCLUSION: OP were less likely than YP to receive AC. Receipt of AC reduced recurrences in OP, supporting its use, although no significant benefit was observed from the addition of oxaliplatin.

The association of body composition on chemotherapy toxicities in non-metastatic colorectal cancer patients: a systematic review.

BACKGROUND: In recent years, certain body composition measures, assessed by computed tomography (CT), have been found to be associated with chemotherapy toxicities. This review aims to explore available data on the relationship between skeletal muscle and adiposity, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular and intermuscular adipose tissue and their association with chemotherapy toxicity in non-metastatic colorectal cancer (CRC) patients. METHODS: A systematic literature search following PRISMA guidelines was conducted in Medline, Embase, Cochrane and Web of Science, for papers published between 2011 and 2023. The search strategy combined keywords and MESH terms relevant to 'body composition', 'chemotherapy toxicities', and 'non-metastatic colorectal cancer'. RESULTS: Out of 3868 studies identified, six retrospective studies fulfilled the inclusion criteria with 1024 eligible patients. Low skeletal muscle mass was strongly associated with increased incidence of both chemotherapy toxicities and dose-limiting toxicity (DLT). The association of VAT, intramuscular and intermuscular adiposity was heterogeneous and inconclusive. There was no association between SAT and chemotherapy intolerance. No universal definitions or cut-offs for sarcopenia and obesity were noted. All studies utilized 2-dimensional (2D) CT slices for CT body composition assessment with varied selection on the vertebral landmark and inconsistent reporting of tissue-defining Hounsfield unit (HU) measurements. CONCLUSION: Low skeletal muscle is associated with chemotherapy toxicities in non-metastatic CRC. However, quality evidence on the role of adiposity is limited and heterogeneous. More studies are needed to confirm these associations with an emphasis on a more coherent body composition definition and an approach to its assessment, especially regarding sarcopenia.

Advanced flexible transvaginal mesh with high visibility under computerized tomography (CT) scan.

Pelvic floor disorders, including pelvic organ prolapse (POP) and stress urinary incontinence (SUI), are serious and very common. Surgery is commonly undertaken to restore the strength of the vaginal wall using transvaginal surgical mesh (TVM). However, up to 15% of TVM implants result in long-term complications, including pain, recurrent symptoms, and infection.Clinical Relevance- In this study, a new bioengineered TVM has been developed to address these issues. The TVM is visible using noninvasive imaging techniques such as computed tomography (CT); it has a highly similar structural profile to human tissue and potential to reduce pain and inflammation. These combined technological advances have the potential to revolutionize women's health.

Valence-driven colorimetric detection of norovirus protease via peptide-AuNP interactions.

We report here a colorimetric method for rapid detection of norovirus based on the valence-driven peptide-AuNP interactions. We engineered a peptide sequence named K1 with a cleavage sequence in between two lysine residues. The positively charged lysine groups aggregated the negatively charged nanoparticles leading to a purple color change. There was a red color when the cleavage sequence was digested by the Southampton norovirus 3C-like protease (SV3CP)-a protease involved in the life cycle of Human norovirus (HNV). The limit of detection was determined to be 320 nM in Tris buffer. We further show that the sensor has good performance in exhaled breath condensate, urine, and faecal matter. This research provides a potential easy and quick way to selectively detect HNV.

Identification of Differences in Body Composition Measures Using 3D-Derived Artificial Intelligence from Multiple CT Scans across the L3 Vertebra Compared to a Single Mid-Point L3 CT Scan.

Purpose: Body composition analysis in colorectal cancer (CRC) typically utilises a single 2D-abdominal axial CT slice taken at the mid-L3 level. The use of artificial intelligence (AI) allows for analysis of the entire L3 vertebra (non-mid-L3 and mid-L3). The goal of this study was to determine if the use of an AI approach offered any additional information on capturing body composition measures. Methods: A total of 2203 axial CT slices of the entire L3 level (4-46 slices were available per patient) were retrospectively collected from 203 CRC patients treated at Western Health, Melbourne (97 males; 47.8%). A pretrained artificial intelligence (AI) model was used to segment muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on these slices. The difference in body composition measures between mid-L3 and non-mid-L3 scans was compared for each patient, and for males and females separately. Results: Body composition measures derived from non-mid-L3 scans exhibited a median range of 0.85% to 6.28% (average percent difference) when compared to the use of a single mid-L3 scan. Significant variation in the VAT surface area (p = 0.02) was observed in females compared to males, whereas male patients exhibited a greater variation in SAT surface area (p < 0.001) and radiodensity (p = 0.007). Conclusion: Significant differences in various body composition measures were observed when comparing non-mid-L3 slices to only the mid-L3 slice. Researchers should be aware that considering only the use of a single midpoint L3 CT scan slice will impact the estimate of body composition measurements.

Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

PURPOSE: Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients. METHODS: 203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance. RESULTS: A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10-3). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50-0.54; males: AUC = 0.50-0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males). CONCLUSION: Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent.

Endoproteolysis of Oligopeptide-Based Coacervates for Enzymatic Modeling.

Better insights into the fate of membraneless organelles could strengthen the understanding of the transition from prebiotic components to multicellular organisms. Compartmentalized enzyme reactions in a synthetic coacervate have been investigated, yet there remains a gap in understanding the enzyme interactions with coacervate as a substrate hub. Here, we study how the molecularly crowded nature of the coacervate affects the interactions of the embedded substrate with a protease. We design oligopeptide-based coacervates that comprise an anionic Asp-peptide (D10) and a cationic Arg-peptide (R5R5) with a proteolytic cleavage site. The coacervates dissolve in the presence of the main protease (Mpro) implicated in the coronavirus lifecycle. We capitalize on the condensed structure, introduce a self-quenching mechanism, and model the enzyme kinetics by using Cy5.5-labeled peptides. The determined specificity constant (kcat/KM) is 5817 M-1 s-1 and is similar to that of the free substrate. We further show that the enzyme kinetics depend on the type and quantity of dye incorporated into the coacervates. Our work presents a simple design for enzyme-responsive coacervates and provides insights into the interactions between the enzyme and coacervates as a whole.

An approach to zwitterionic peptide design for colorimetric detection of the Southampton norovirus SV3CP protease.

Noroviruses are highly contagious and are one of the leading causes of acute gastroenteritis worldwide. Due to a lack of effective antiviral therapies, there is a need to diagnose and surveil norovirus infections to implement quarantine protocols and prevent large outbreaks. Currently, the gold standard of diagnosis uses reverse transcription polymerase chain reaction (RT-PCR), but PCR can have limited availability. Here, we propose a combination of a tunable peptide substrate and gold nanoparticles (AuNPs) to colorimetrically detect the Southampton norovirus 3C-like protease (SV3CP), a key protease in viral replication. Careful design of the substrate employs a zwitterionic peptide with opposite charged moieties on the C- and N- termini to induce a rapid color change visible to the naked eye; thus, this color change is indicative of SV3CP activity. This work expands on existing zwitterionic peptide strategies for protease detection by systematically evaluating the effects of lysine and arginine on nanoparticle charge screening. We also determine a limit of detection for SV3CP of 28.0 nM with comparable results in external breath condensate, urine, and fecal matter for 100 nM of SV3CP. The key advantage of this system is its simplicity and accessibility, thus making it an attractive tool for qualitative point-of-care diagnostics.

Gastrointestinal histoplasmosis presenting as a small bowel obstruction in an immunocompetent patient.

The clinical presentation of gastrointestinal histoplasmosis (GIH) is often non-specific, mimicking several other gastrointestinal diseases, making diagnosis difficult. We present a case of GIH, causing bowel obstruction in an immunocompetent patient. A woman in her 80s presented with anorexia and a distended abdomen. A CT scan of the abdomen and pelvis (CTAP) showed ileal thickening. Endoscopy was performed and ileal biopsies were taken. A diagnosis of histoplasmosis was made by histopathology, and she began treatment with liposomal amphotericin and itraconazole. Despite presenting well at her routine follow-up appointments, she presented 4 months after diagnosis with worsening symptoms of anorexia and abdominal distention. CTAP showed the ileal stricture causing bowel obstruction. She underwent itraconazole and steroid treatment while having nasogastric tube decompression. Subsequent gastrografin follow through showing partial obstruction and surgical resection was planned. Unfortunately, her condition deteriorated, her kidney function worsened and she suffered from aspiration pneumonia with eventual demise.