PURPOSE: Most patients with colorectal liver metastases present to general surgeons and oncologists without a specialist interest in their management. Since treatment strategy is frequently dependent on the response to earlier treatments, our aim was to create a therapeutic decision model identifying appropriate procedure sequences. METHODS: We used the RAND Corporation/University of California, Los Angeles Appropriateness Method (RAM) assessing strategies of resection, local ablation and chemotherapy. After a comprehensive literature review, an expert panel rated appropriateness of each treatment option for a total of 1,872 ratings decisions in 252 cases. A decision model was constructed, consensus measured and results validated using 48 virtual cases, and 34 real cases with known outcomes. RESULTS: Consensus was achieved with overall agreement rates of 93.4 to 99.1%. Absolute resection contraindications included unresectable extrahepatic disease, more than 70% liver involvement, liver failure, and being surgically unfit. Factors not influencing treatment strategy were age, primary tumor stage, timing of metastases detection, past blood transfusion, liver resection type, pre-resection carcinoembryonic antigen (CEA), and previous hepatectomy. Immediate resection was appropriate with adequate radiologically-defined resection margins and no portal adenopathy; other factors included presence of < or = 4 or > 4 metastases and unilobar or bilobar involvement. Resection was appropriate postchemotherapy, independent of tumor response in the case of < or = 4 metastases and unilobar liver involvement. Resection was appropriate only for > 4 metastases or bilobar liver involvement, after tumor shrinkage with chemotherapy. When possible, resection was preferred to local ablation. CONCLUSION: The results were incorporated into a decision matrix, creating a computer program (OncoSurge). This model identifies individual patient resectability, recommending optimal treatment strategies. It may also be used for medical education.
PURPOSE: Most patients with colorectal liver metastases present to general surgeons and oncologists without a specialist interest in their management. Since treatment strategy is frequently dependent on the response to earlier treatments, our aim was to create a therapeutic decision model identifying appropriate procedure sequences. METHODS: We used the RAND Corporation/University of California, Los Angeles Appropriateness Method (RAM) assessing strategies of resection, local ablation and chemotherapy. After a comprehensive literature review, an expert panel rated appropriateness of each treatment option for a total of 1,872 ratings decisions in 252 cases. A decision model was constructed, consensus measured and results validated using 48 virtual cases, and 34 real cases with known outcomes. RESULTS: Consensus was achieved with overall agreement rates of 93.4 to 99.1%. Absolute resection contraindications included unresectable extrahepatic disease, more than 70% liver involvement, liver failure, and being surgically unfit. Factors not influencing treatment strategy were age, primary tumor stage, timing of metastases detection, past blood transfusion, liver resection type, pre-resection carcinoembryonic antigen (CEA), and previous hepatectomy. Immediate resection was appropriate with adequate radiologically-defined resection margins and no portal adenopathy; other factors included presence of < or = 4 or > 4 metastases and unilobar or bilobar involvement. Resection was appropriate postchemotherapy, independent of tumor response in the case of < or = 4 metastases and unilobar liver involvement. Resection was appropriate only for > 4 metastases or bilobar liver involvement, after tumor shrinkage with chemotherapy. When possible, resection was preferred to local ablation. CONCLUSION: The results were incorporated into a decision matrix, creating a computer program (OncoSurge). This model identifies individual patient resectability, recommending optimal treatment strategies. It may also be used for medical education.
Authors: S T Lee; V Muralidharan; N Tebbutt; P Wong; C Fang; Z Liu; H Gan; J Sachinidis; K Pathmaraj; C Christophi; A M Scott Journal: Eur J Nucl Med Mol Imaging Date: 2020-10-30 Impact factor: 9.236
Authors: Mahfud Mahfud; Stefan Breitenstein; Ashraf Mohammad El-Badry; Milo Puhan; Andreas Rickenbacher; Panagiotis Samaras; Patrick Pessaux; Santiago Lopez-Ben; Daniel Jaeck; Joan Figueras; Pierre Alain-Clavien Journal: World J Surg Date: 2010-01 Impact factor: 3.352
Authors: R Zarate; J Rodríguez; E Bandres; A Patiño-Garcia; M Ponz-Sarvise; A Viudez; N Ramirez; N Bitarte; A Chopitea; J Gacía-Foncillas Journal: Br J Cancer Date: 2010-03-09 Impact factor: 7.640