ABSTRACT
Most cases of deaths from colorectal cancer (CRC) result from metastases, which are often still undetectable at disease detection time. Even so, in many cases, shedding is assumed to have taken place before that time. The dynamics of metastasis formation and growth are not well-established. This work aims to explore CRC lung metastasis growth rate and dynamics. We analyzed a test case of a metastatic CRC patient with four lung metastases, with data of four serial computed tomography (CT) scans measuring metastasis sizes while untreated. We fitted three mathematical growth models-exponential, logistic, and Gompertzian-to the CT measurements. For each metastasis, a best-fitted model was determined, tumor doubling time (TDT) was assessed, and metastasis inception time was extrapolated. Three of the metastases showed exponential growth, while the fourth showed logistic restraint of the growth. TDT was around 93 days. Predicted metastasis inception time was at least 4-5 years before the primary tumor diagnosis date, though they did not reach detectable sizes until at least 1 year after primary tumor resection. Our results support the exponential growth approximation for most of the metastases, at least for the clinically observed time period. Our analysis shows that metastases can be initiated before the primary tumor is detectable and implies that surgeries accelerate metastasis growth.
ABSTRACT
RATIONALE AND OBJECTIVES: This study aimed to provide decision support for the human expert, to categorize liver metastases into their primary cancer sites. Currently, once a liver metastasis is detected, the process of finding the primary site is challenging, time-consuming, and requires multiple examinations. The proposed system can support the human expert in localizing the search for the cancer source by prioritizing the examinations to probable cancer sites. MATERIALS AND METHODS: The suggested method is a learning-based approach, using computed tomography (CT) data as the input source. Each metastasis is circumscribed by a radiologist in portal phase and in non-contrast CT images. Visual features are computed from these images, combined into feature vectors, and classified using support vector machine classification. A variety of different features were explored and tested. A leave-one-out cross-validation technique was conducted for classification evaluation. The methods were developed on a set of 50 lesion cases taken from 29 patients. RESULTS: Experiments were conducted on a separate set of 142 lesion cases taken from 71 patients with four different primary sites. Multiclass categorization results (four classes) achieved low accuracy results. However, the proposed system was found to provide promising results of 83% and 99% for top-2 and top-3 classification tasks, respectively. Moreover, when compared to the experts' ability to distinguish the different metastases, the system shows improved results. CONCLUSIONS: Automated systems, such as the one proposed, show promising new results and demonstrate new capabilities that, in the future, will be able to provide decision and treatment support for radiologists and oncologists, toward more efficient detection and treatment of cancer.
Subject(s)
Algorithms , Decision Support Techniques , Image Processing, Computer-Assisted , Liver Neoplasms/diagnostic imaging , Liver Neoplasms/secondary , Tomography, X-Ray Computed , Humans , Neoplasms, Unknown Primary , Support Vector MachineABSTRACT
BACKGROUND: Accurate assessment of liver fibrosis is crucial for the management of patients with hepatitis C virus (HCV) infection. OBJECTIVES: To evaluate the performance of liver segment-to-spleen volume ratio in predicting the severity of liver fibrosis. METHODS: Sixty-four consecutive HCV patients were enrolled in this retrospective study. All patients underwent contrast-enhanced computed tomography (CT) and were divided into three groups based on their hepatic fibrosis stage evaluated by shear-wave elastography (SWE): non-advanced (F0-F1, n=29), advanced (F2, n=19) and severe fibrosis (F3-F4, n=16). Using semi-automated liver segmentation software, we calculated the following liver segments and spleen volumes for each participant: total liver volume (TLV), caudate lobe (CV), left lateral segment (LLV), left medial segment (LMV), right lobe (RV) and spleen (SV), a well as their ratios: CV/SV, RV/SV, LLV/SV, LMV/SV and TLV/SV. RESULTS: RV/SV was found to discriminate between patients with non-advanced and advanced fibrosis (P = 0.001), whereas SV, CV, RV, TLV/SV, LMV/SV and RV/SV discriminated between patients with advanced and severe fibrosis (P < 0.05). RV/SV ≤ 3.6 and RV ≤ 2.9 were identified as the best cutoff values to differentiate non-advanced from advanced fibrosis and advanced from severe fibrosis with sensitivities of 72.2% and 92.7%, specificities of 72.7% and 77.8%, and with an area under the receiver operating characteristic (ROC) curve of 0.797 and 0.847, respectively (P ≤ 0.002). CONCLUSIONS: RV/SV may be used for the assessment and monitoring of liver fibrosis in HCV patients prior to the administration of antiviral therapy, considering SWE as the reference method.
