In a multicenter study involving nearly 300 patients, researchers found that abbreviated magnetic resonance imaging had a sensitivity rate of 88.2 percent and a specificity rate of 89.1 percent for detecting early-stage hepatocellular carcinoma. While noting the potential of dynamic abbreviated MRI for diagnosing HCC in patients with compensated cirrhosis, Yokoo and colleagues said prospective studies are necessary to validate their findings and provide clarity into the lower sensitivity they noted in patients with decompensated cirrhosis.

The research included 76 patients aged 70 years or above suffering from OSCC, of which 52 patients were treated with surgery & 24 were treated with non-surgical treatment that provides radiotherapy and/or chemotherapy. For an indication of non-surgical treatment, the value of serum Alb is less than equal to 3.5 g/dl with stages III, I, and V. Research confirmed the importance of serum albumin as it indicates the nutritional status in the mid to long run, including cachexia.

For a paper appearing in JAMA Network Open, Japanese researchers turn to transcriptomics to search for survival-related markers that can be detected prior to treatment in individuals with advanced rectal cancers, while also tracking response to neoadjuvant chemoradiotherapy. The results suggest neoadjuvant chemoradiotherapy response, recurrence-free survival, and overall survival were linked to cytotoxic lymphocyte scores.

Noting that the machine learning model incorporating magnetic resonance imaging had a higher mean area under the curve than a model based solely on clinical features for predicting hepatocellular carcinoma recurrence, researchers said the study findings could have implications for refining liver transplant criteria. However, for patients who had a liver transplant, the MRI imaging-specific machine learning model and the model that combined MRI scans and clinical features had an 87.5 success rate of predicting recurrence in comparison to a 25 percent success rate for the clinical model.

“The findings suggest that machine learning-based models can predict recurrence before therapy allocation in patients with early-stage hepatocellular carcinoma initially eligible for liver transplant,” wrote corresponding author Julius Chapiro from the department of radiology and biomedical imaging at Yale University School of Medicine in New Haven, CT. Ultimately, all three models predicted posttreatment recurrence for early-stage HCC from pretreatment clinical, MRI, and both data combined.

Researchers have found that imaging-based machine learning models can accurately predict patient response to liver cancer treatment. - A proof-of-concept study published in the American Journal of Roentgenology shows that machine learning models can predict tumor recurrence in patients with early-stage hepatocellular carcinoma, a type of liver cancer, prior to liver transplant or related treatments. Last year, Cleveland Clinic researchers to predict health and survival outcomes for HCC patients following liver transplant, which achieved significant results.

ML applied to presently underutilized imaging features could help construct more reliable criteria for organ allocation and liver transplant eligibility. “The findings suggest that machine learning-based models can predict recurrence before therapy allocation in patients with early-stage hepatocellular carcinoma initially eligible for liver transplant,” wrote corresponding author Julius Chapiro from the department of radiology and biomedical imaging at Yale University School of Medicine in New Haven, CT.