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The 3-year DFS rate was 77.1 and 68.6% in the chemo+PRM1201 and chemo+placebo group, respectively (hazard ratio [HR], 0.63; 95% CI, 0.42 to 0.94). The QOL of patients in the chemo+PRM1201 group were significantly improved in terms of global quality of life, physical functioning, role functioning, emotional functioning, fatigue, and appetite loss. The incidence of grade 3 or 4 treatment-related adverse event (TRAEs) were similar between the two arms.
Chemotherapy in combination with PRM1201 improved the adjuvant treatment of colon cancer. PRM1201 can be recommended as an effective option in clinical practice.
Chinese Clinical Trials Registry, identifier ChiCTR-IOR-16007719.
Chinese Clinical Trials Registry, identifier ChiCTR-IOR-16007719.
Microvascular invasion (MVI) is highly associated with poor prognosis in patients with liver cancer. Predicting MVI before surgery is helpful for surgeons to better make surgical plan. In this study, we aim at establishing a nomogram to preoperatively predict the occurrence of microvascular invasion in liver cancer.
A total of 405 patients with postoperative pathological reports who underwent curative hepatocellular carcinoma resection in the Third Affiliated Hospital of Sun Yat-sen University from 2013 to 2015 were collected in this study. Among these patients, 290 were randomly assigned to the development group while others were assigned to the validation group. The MVI predictive factors were selected by Lasso regression analysis. Nomogram was established to preoperatively predict the MVI risk in HCC based on these predictive factors. The discrimination, calibration, and effectiveness of nomogram were evaluated by internal validation.
Lasso regression analysis revealed that discomfort of right upper abdomen, vascular invasion, lymph node metastases, unclear tumor boundary, tumor necrosis, tumor size, higher alkaline phosphatase were predictive MVI factors in HCC. The nomogram was established with the value of AUROC 0.757 (0.716-0.809) and 0.768 (0.703-0.814) in the development and the validation groups. Well-fitted calibration was in both development and validation groups. Decision curve analysis confirmed that the predictive model provided more benefit than treat all or none patients. The predictive model demonstrated sensitivity of 58.7%, specificity of 80.7% at the cut-off value of 0.312.
Nomogram was established for predicting preoperative risk of MVI in HCC. Better treatment plans can be formulated according to the predicted results.
Nomogram was established for predicting preoperative risk of MVI in HCC. Better treatment plans can be formulated according to the predicted results.
Malignant liver infiltration is an uncommon cause of acute liver failure (ALF) and has rarely been reported.
We present a patient with progressive jaundice and dissociation of bilirubin and aminotransferases, who had no history of relevant liver diseases or tumor except the use of Chinese traditional drugs for a cold. RO-7113755 An abdominal computed tomography (CT) scan showed ascites without hepatic focal lesions. Laboratory studies revealed no evidence of hepatitis or underlying autoimmune disorders. Following 8 days of conservative management ALF rapidly worsened. Contrast-enhanced CT revealed diffuse regenerative nodules in the liver. The patient underwent liver biopsy, which demonstrated that the liver was infiltrated by pulmonary neuroendocrine tumor classified as small cell lung cancer. The patient died 13 days after diagnosis.
This case represents a rare cause of ALF induced by pulmonary neuroendocrine tumor of small cell type and illustrates the importance of prompt biopsy in an unknown cause of ALF.
This case represents a rare cause of ALF induced by pulmonary neuroendocrine tumor of small cell type and illustrates the importance of prompt biopsy in an unknown cause of ALF.
The purpose of this study was to evaluate the feasibility and diagnostic performance ofprostate-specific membrane antigen (PSMA) based
F-DCFPyL PET/CT-ultrasound (PET/CT-US) or PET/MRI-ultrasound (PET/MRI-US) fusion targeted biopsy for intra-prostatic PET-positive lesions.
From April 2018 to November 2019, we prospectively enrolled 55 candidates to perform PET/CT-US or PET/MRI-US fusion targeted biopsies for solitary PET-positive prostate lesions (two to four cores/lesion). The positive rates of prostate cancer based on patients and biopsy cores were calculated respectively. With reference to the pathological results of biopsy cores, the MR signal characteristics in the area of the PET-positive lesion were analyzed for the patients who underwent PET/MRI.
A total of 178 biopsy cores were taken on the 55 patients. One hundred forty-six biopsy cores (82.0%, 146/178) from 51 (92.7%, 51/55) patients were positive for prostate cancer; 47 (85.5%, 47/55) were clinically significant prostate cancer. It is notebiopsies.
To develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy.
From a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relatF1 Score (90.6 ± 1.1%).
The presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.
The presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.
