Purpose:
Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE.
Materials and Methods:
Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control.
Results:
The model’s response prediction accuracy rate was 74.2% (95% CI=64%−82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%−72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome.
Conclusion:
This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.
Health care consumer organizations and insurance companies increasingly are scrutinizing value when considering reimbursement policies for medical interventions. Recently, members of several American Academy of Otolaryngology-Head & Neck Surgery (AAO-HNS) committees worked closely with one insurance company to refine reimbursement policies for preoperative localization imaging in patients undergoing surgery for primary hyperparathyroidism. This endeavor led to an AAO-HNS parathyroid imaging consensus statement (https://www.entnet.org/ content/parathyroid-imaging). The American Head and Neck Society Endocrine Surgery Section gathered an expert panel of authors to delineate imaging options
Patient-derived organoids (PDOs) are emerging as preclinical models with promising values in personalized cancer therapy. The purpose of this study was to establish a living biobank of PDOs from patients with non-small cell lung cancer (NSCLC) and to study the responses of PDOs to drugs. PDOs derived from NSCLC were cultured in vitro, and then treated with natural compounds including chelerythrine chloride, cantharidin, harmine, berberine and betaine with series of concentrations (0.5-30 μM) for drug screening. Phenotypic features and treatment responses of established PDOs were reported. Cell lines (H1299, H460 and H1650) were used for drug screening. We successfully established a living NSCLC organoids biobank of 10 patients, which showed similar pathological features with primary tumors. Nine of the 10 patients showed mutations in EGFR. Natural compounds chelerythrine chloride, cantharidin and harmine showed anticancer activity on PDOs and cell lines. There was no significant difference in the 95% confidence interval (CI) for the IC50 value of chelerythrine chloride between PDOs (1.56-2.88 μM) and cell lines (1.45-3.73 μM, p>0.05). PDOs were sensitive to berberine (95% CI, 0.092-1.55 μM), whereas cell lines showed a resistance (95% CI, 46.57-2275 μM, p<0.0001). PDOs had a higher IC50 value of cantharidin, and a lower IC50 value of harmine than cell lines (p<0. 05, μM in cell lines, respectively). Both PDOs and cell lines were resistant to betaine. Chelerythrine chloride showed the highest inhibitory effect in both models. Our study established a living biobank of PDOs from NSCLC patients, which might be used for high-throughput drug screening and for promising personalized therapy design.
BackgroundInternational thyroid nodule and cancer management guidelines generally fail to take into account potential limitations in diagnostic and treatment resources.MethodsThyroid cancer specialists from the African Head and Neck Society and American Head & Neck Society Endocrine Section developed guidelines for diagnosis and management of thyroid nodules and cancer in low resource settings. Recommendations were based on literature review and expert opinion, with level of evidence defined.ResultsUsing the ADAPTE process, diagnostic and treatment algorithms were adapted from the National Comprehensive Cancer Network (NCCN). Low resource settings were simulated by systematically removing elements such as availability of laboratory testing, hormone replacement, imaging, and cytopathology from NCCN guidelines.ConclusionsSuccessful management of thyroid nodules and cancer in low resource settings requires adaptation of treatment methodologies. These guidelines define specific scenarios where either more or less aggressive intervention for thyroid pathology may be advisable based on limited available resources.
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