Hepatocellular Carcinoma with Bile Duct Tumor Thrombus: A Case Report and Literature Review of 890 Patients Affected by Uncommon Primary Liver Tumor Presentation

Colorectal Cancer: Current Updates and Future Perspectives

Colorectal cancer is a frequent neoplasm in western countries, mainly due to dietary and behavioral factors. Its incidence is growing in developing countries for the westernization of foods and lifestyles. An increased incidence rate is observed in patients under 45 years of age. In recent years, the mortality for CRC is decreased, but this trend is slowing. The mortality rate is reducing in those countries where prevention and treatments have been implemented. The survival is increased to over 65%. This trend reflects earlier detection of CRC through routine clinical examinations and screening, more accurate staging through advances in imaging, improvements in surgical techniques, and advances in chemotherapy and radiation. The most important predictor of survival is the stage at diagnosis. The screening programs are able to reduce incidence and mortality rates of CRC. The aim of this paper is to provide a comprehensive overview of incidence, mortality, and survival rate for CRC.

Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

BACKGROUND

Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM.

METHODS

A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened.

RESULTS

We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT).

CONCLUSIONS

Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.

Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network

Segmenting the liver and liver tumors in computed tomography (CT) images is an important step toward quantifiable biomarkers for a computer-aided decision-making system and precise medical diagnosis. Radiologists and specialized physicians use CT images to diagnose and classify liver organs and tumors. Because these organs have similar characteristics in form, texture, and light intensity values, other internal organs such as the heart, spleen, stomach, and kidneys confuse visual recognition of the liver and tumor division. Furthermore, visual identification of liver tumors is time-consuming, complicated, and error-prone, and incorrect diagnosis and segmentation can hurt the patient's life. Many automatic and semi-automatic methods based on machine learning algorithms have recently been suggested for liver organ recognition and tumor segmentation. However, there are still difficulties due to poor recognition precision and speed and a lack of dependability. This paper presents a novel deep learning-based technique for segmenting liver tumors and identifying liver organs in computed tomography maps. Based on the LiTS17 database, the suggested technique comprises four Chebyshev graph convolution layers and a fully connected layer that can accurately segment the liver and liver tumors. Thus, the accuracy, Dice coefficient, mean IoU, sensitivity, precision, and recall obtained based on the proposed method according to the LiTS17 dataset are around 99.1%, 91.1%, 90.8%, 99.4%, 99.4%, and 91.2%, respectively. In addition, the effectiveness of the proposed method was evaluated in a noisy environment, and the proposed network could withstand a wide range of environmental signal-to-noise ratios (SNRs). Thus, at SNR = -4 dB, the accuracy of the proposed method for liver organ segmentation remained around 90%. The proposed model has obtained satisfactory and favorable results compared to previous research. According to the positive results, the proposed model is expected to be used to assist radiologists and specialist doctors in the near future.

Micro-hepatocellular carcinoma with bile duct tumor thrombus mimicking intrahepatic intraductal papillary neoplasm of the bile duct: a case report

BACKGROUND

Microhepatocellular carcinoma with a gross bile duct tumor thrombus is extremely rare, making the correct preoperative diagnosis difficult.

CASE PRESENTATION

A 78-year-old man was referred to our department for close examination of a liver tumor that was incidentally detected using ultrasonography. Blood tests revealed normal levels of tumor markers. Abdominal ultrasonography showed a 2-cm-sized hyperechoic mass with indistinct borders and hypoechoic margins at the origin of the right hepatic duct. Dynamic computed tomography showed a tumor with arterial phase predominance, a heterogeneous contrast effect, and prolonged enhancement. Cystic structures were observed in the tumors. In addition, localized dilatation of the caudate lobe bile duct was observed near the tumor. Cholangiography showed that the common bile duct, right and left hepatic ducts, and secondary branches did not have dilatation or stenosis. Biopsies of the bile duct revealed no malignancy. Under suspicion of intrahepatic intraductal papillary neoplasm of the bile duct, right hemi-hepatectomy was performed. The extrahepatic bile duct was preserved, because no tumor was found at the margin of the right hepatic duct during intraoperative frozen diagnosis. Macroscopically, the lesion was an 18 × 15 mm tumor occupying a dilated intrahepatic bile duct near the right hepatic duct, with a soft, fine papillary tumor. Based on morphology and immunostaining, tumor matched with moderately differentiated hepatocellular carcinoma. In addition, a 2 mm-sized hepatocellular carcinoma was observed in the liver parenchyma near the bile duct, where the tumor was located.

CONCLUSIONS

Based on these findings, the patient was diagnosed with small hepatocellular carcinoma with a gross bile duct tumor thrombus. The cystic part seen on the preoperative images was considered as a gap between the bile duct and the tumor thrombus. The patient recovered well with no signs of recurrence 20 months after surgery.

Hepatic Resection in Patients with Colo-Rectal Liver Metastases: Surgical Outcomes and Prognostic Factors of Single-Center Experience

Introduction: Surgical resection has a fundamental role in increasing the chance of survival in patients with colorectal liver metastases. The guidelines have been modified and expanded in time in order to increase the number of patients that can benefit from this treatment. The aim of this study is to analyze the main prognostic factors related to overall and disease-free survival of a series of consecutive patients undergoing liver resection for colorectal liver metastases (CRLM). Materials and Methods: A retrospective review of patients undergoing liver resection for CRLM between April 2018 and September 2021 was performed. Clinical data and laboratory parameters were evaluated using the log-rank test. OS and DFS were estimated using the Kaplan-Meier method. Results: A retrospective study on 75 patients who underwent liver resection for CRLM was performed. The OS and DFS at 1 and 3 years were 84.3% and 63.8% for OS, 55.6% and 30.7% for DFS, respectively. From the analysis of the data, the most significant results indicate that: patients with a lower CEA value <25 ng/mL had an OS of 93.6% and 80.1% at 1 and 3 years, with an average of 36.7 months (CI 95% 33.1–40.3); moreover, patients with a value equal to or greater than 25 ng/mL had a 1-year survival equal to 57.4%, with an average of 13.8 months (CI 95% 9.4–18.2) (p < 0.001); adjuvant chemotherapy increases by 3 years the overall survival (OS: 68.6% vs. 49.7%) (p = 0.013); localization of the primary tumor affects OS, with a better prognosis for left colon metastases (OS at 42 months: 85.4% vs. 42.2%) (p value = 0.056); patients with stage T1 or T2 cancer have a better 3 years OS (92.9–100% vs. 49.7–56.3%) (p = 0.696), while the N0 stage results in both higher 3 years OS and DFS than the N + stages (OS: 87.5% vs. 68.5% vs. 24.5%); metachronous metastases have a higher 3 years OS than synchronous ones (80% vs. 47.4%) (p = 0.066); parenchymal sparing resections have a better 3 years DFS than anatomical ones (33.7% vs. 0%) (p = 0.067); a patient with a parenchymal R1 resection has a much worse prognosis than an R0 (3 years OS: 0% vs. 68.7%) (p < 0.001). Conclusions: CEA value of less than 25 ng/mL, localization of the primary tumor in the left colon, primary tumor in stage T1/2 and N0, metachronous presentation, R0 resection, fewer than four metastases, and use of adjuvant chemotherapy are all parameters that in our analysis have shown a correlation with a better prognosis; moreover, the evaluation of the series is in line with the latest evidence in the literature in defining the non-inferiority of minimally invasive and parenchymal sparing treatment compared to the classic laparotomic approach with anatomic resection.