Association of sarcopenia with severe chemotherapy toxicities and survival in patients with advanced gastric cancer.

BACKGROUND: Sarcopenia or skeletal muscle depletion is a poor prognostic factor for gastric cancer (GC). However, existing cutoff values of skeletal muscle index (SMI) for defining sarcopenia have been found to have limitations when clinically applied. This study aimed to determine the optimal cutoff for SMI to predict severe toxicities of chemotherapy and overall survival (OS) in patients with advanced GC. METHODS: Patients with metastatic gastric adenocarcinoma who received first-line palliative chemotherapy between January 2014 and December 2021 at Queen Mary Hospital, Hong Kong, were included in this study. The SMI was determined via a pre-chemotherapy computed tomography scan. Optimal cutoff points of SMI were identified by recursive partitioning analysis. Univariate and multivariate analyses evaluating risk factors of severe chemotherapy toxicities and OS were also performed. RESULTS: A total of 158 patients (male: 108 (68.4%), median age: 65.3) were included. The SMI cutoff to define low SMI was ≤33 cm2/m2 for males and ≤28 cm2/m2 for females; 30 patients (19.0%) had low SMI. Patients with low SMI had a higher incidence of hematological toxicities (63.3% vs 32.0%, P = .001) and non-hematological toxicities (66.7% vs 36.7%, P = .003). Multivariable analysis indicated that low SMI and low serum albumin (≤28 g/L) were independent predictive factors of hematological toxicity, while low SMI and neutrophil-lymphocyte ratio ≥5 were predictive factors of non-hematological toxicity. Moreover, patients with low SMI had a significantly shorter OS (P = .011), lower response rate to chemotherapy (P = .045), and lower utilization of subsequent lines of treatment (P < .001). CONCLUSIONS: Using pre-chemotherapy SMI cutoff (≤33 cm2/m2 for males and 28 cm2/m2 for females) one can identify individuals with a higher risk of severe chemotherapy toxicities and worse prognosis.

Evaluating the Diagnostic Performance of Large Language Models on Complex Multimodal Medical Cases.

Large language models showed interpretative reasoning in solving diagnostically challenging medical cases.

Presence of tumour capsule on contrast-enhanced CT is associated with improved outcomes of stereotactic body radiation therapy in hepatocellular carcinoma patients.

PURPOSE: Stereotactic body radiation therapy (SBRT) is a novel local therapy for the treatment of hepatocellular carcinoma (HCC). While effective, there is currently no reliable radiological marker to guide patient selection. In this study, we investigated the prognostic value of capsule appearance on contrast-enhanced computed tomography (CT) for patients undergoing SBRT. MATERIALS AND METHODS: Between 2006 and 2017, 156 consecutive patients with Child-Pugh score class A/B and HCC ≥ 5 cm who underwent SBRT were retrospectively analysed. Baseline triple-phase CTs of the abdomen were reviewed for the presence of capsule appearances and correlated with objective response rate (ORR), overall survival (OS) and pattern of treatment failure. RESULTS: Capsule appearance on CT was present in 83 (53.2%) patients. It was associated with improved ORR by Response Evaluation Criteria in Solid Tumours (RECIST) (60.2 vs. 24.7%, p < 0.001) and Modified Response Evaluation Criteria in Solid Tumours (mRECIST) (78.3 vs. 34.2%, p < 0.001). The presence of a capsule was also associated with superior 2­year local control (89.1 vs. 51.4%, p < 0.001) and 2­year OS (34.1 vs. 14.8%, p < 0.01). Hepatic out-field failure was the dominant mode of progression, which was less common in patients with intact capsule (54.2 vs. 60.3%, p = 0.01). CONCLUSION: Capsule appearance on CT could potentially be a non-invasive prognostic marker for selecting HCC patients to undergo SBRT. A larger cohort is warranted to validate our findings.

Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment.

Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, i.e., bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.

Regional liver function analysis with gadoxetic acid-enhanced MRI and virtual hepatectomy: prediction of postoperative short-term outcomes for HCC.

OBJECTIVES: To explore the role of quantitative regional liver function assessed by preoperative gadoxetic acid-enhanced MRI with computer-aided virtual hepatectomy to predict short-term outcomes after major hepatectomy for HCC. METHODS: We retrospectively reviewed the records of 133 consecutive patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI and indocyanine green (ICG) test. Forty-five patients received open major hepatectomy. Liver function reserve and the future liver remnant were evaluated by computer-aided virtual hepatectomy. Global liver functional parameters included the T1 relaxation time reduction rate (T1ratio) and functional liver volume (FV), whereas regional parameters included the rT1pos, rT1ratio, remnant FV (rFV), and remnant FV ratio (rFVratio) of the remnant liver. The functional parameters of the MRI and ICG were used to predict the short-term outcomes (liver failure and major complications) after major hepatectomy. RESULTS: The T1ratio and FV were correlated with the ICG test (rho = - 0.304 and - 0.449, p < 0.05). FV < 682.8 ml indicated preoperative ICG-R15 ≥ 14% with 0.765 value of the area under the curve (AUC). No patient who underwent major resection with good liver functional reserve (ICG < 14%) and enough future remnant volume (> 30% standard LV) developed liver failure. Low rT1ratio (< 66.5%) and high rT1pos (> 217.5 ms) may predict major complications (AUC = 0.831 and 0.756, respectively; p < 0.05). The rT1ratio was an independent risk factor for postoperative major complications (odds ratio [OR] = 0.845, 95% CI, 0.736-0.966; p < 0.05). CONCLUSION: Preoperative gadoxetic acid-enhanced MRI with computer-aided virtual hepatectomy may facilitate optimal assessment of regional liver functional reserve to predict short-term outcomes after major hepatectomy for HCC. KEY POINTS: • Preoperative gadoxetic acid-enhanced MRI with virtual hepatectomy and volumetric analysis can provide precise liver volume and regional functional assessment. • Quantitative regional liver function assessed by gadoxetic acid-enhanced MRI can predict the short-term outcomes after major hepatectomy in patients with HCC. • The regional liver function assessed by gadoxetic acid-enhanced MRI is an independent risk factor for postoperative major complications.

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR). MATERIALS AND METHODS: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC). RESULTS: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test). CONCLUSIONS: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.

Leiomyosarcoma of the portal vein: a case report and review of the literature.

Leiomyosarcomas (LMS) are rare malignant tumours of smooth muscle origin predominately affecting females in their sixth decade. Only 2% of LMS arise from blood vessels and most are in the inferior vena cava. We present the first reported case of LMS of the portal vein in a male patient. Multidetector CT showed a large mass in the main portal vein, which was initially misinterpreted as a pancreatic cancer. Careful examination of the multidetector CT images showed radiological features of an intraluminal mass, and a preoperative diagnosis of primary LMS of the main portal vein was made. The patient underwent curative surgery and made an uneventful recovery. Awareness of this entity and recognition of the salient CT features may facilitate radiologists in making the correct preoperative diagnosis.