Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

CT-radiomics and pathological tumor response to systemic therapy: A predictive analysis for colorectal liver metastases. Development and internal validation of a clinical-radiomic model

INTRODUCTION

The standard treatment of colorectal liver metastases (CRLM) is surgery with perioperative chemotherapy. A tumor response to systemic therapy confirmed at pathology examination is the strongest predictor of survival, but it cannot be adequately predicted in the preoperative setting. This bi-institutional retrospective study investigates whether CT-based radiomics of CRLM and peritumoral tissue provides a reliable non-invasive estimation of the pathological tumor response to chemotherapy.

METHODS

All consecutive patients undergoing liver resection for CRLM at the two institutions were considered. Only patients with a radiological partial response or stable disease at chemotherapy and with a preoperative/post-chemotherapy CT performed <60 days before surgery were included. The pathological response was evaluated according to the tumor regression grade (TRG). The tumor (Tumor-VOI) was manually segmented on the portal phase of the CT and a 5-mm ring of peritumoral tissue was automatically generated (Margin-VOI). The predictive models underwent internal validation.

RESULTS

Overall, 222 patients were included; 64 had a pathological response (29 %, TRG1-3). Two-third of patients displaying a radiological response (111/170) did not have a pathological one (TRG4-5). For TRG1-3 prediction, the clinical model performed fairly (Accuracy = 0.725, validation-AUC = 0.717 95%CI = 0.652-0.788). Radiomics improved the results: the model combining the clinical data and Tumor-VOI features had Accuracy = 0.743 and validation-AUC = 0.729 (95%CI = 0.665-0.798); the full model (clinical/Tumor-VOI/Margin-VOI) achieved Accuracy = 0.820 and validation-AUC = 0.768 (95%CI = 0.707-0.826).

CONCLUSION

CT-based radiomics of CRLM allows an insightful non-invasive assessment of TRG. The combined analysis of the tumor and peritumoral tissue improves the prediction. In association with clinical data, the radiomic indices outperform standard radiological and clinical evaluation.

Comparative exploration of the carotid body in domestic animals: morphology, physiology, histology, and pathology

The aim of the study was to present a review of the literature and research on the carotid body (CB) over the past years and update the latest findings. The purpose of this article is to present a general overview and comparative analysis of CB between species, from the microanatomy to the pathology of CB. This study gives information about the embryological development and physiological aspects of anatomical findings and their differences. The second part of the article gives a comparative analysis of the pathology of CB. Neoplasia of the CB in humans, namely, paraganglioma, in most cases, is part of a genetic MEN syndrome (multiple endocrine neoplasia). In dogs, paraganglioma is also involved with multiple neoplasia formations throughout the body, including endocrine and neuroendocrine glands. From this perspective, dogs are the most suitable specimens for studying carotid body tumors and their involvement in a MEN-like syndrome.

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification

OBJECTIVES

This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images.

METHODS

This retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase. Those features which showed high stability, non-redundancy, and indicative information were selected. An unsupervised consensus clustering analysis on these features was adopted to identify subgroups of CRLM patients. Overall survival (OS), disease-free survival (DFS), and liver-specific DFS were compared between the identified subgroups. Cox regression analysis was applied to evaluate prognostic risk factors.

RESULTS

A total of 851 radiomics features were extracted, and 56 robust features were finally selected for unsupervised clustering analysis which identified two distinct subgroups (96 and 101 patients respectively). There were significant differences in the OS, DFS, and liver-specific DFS between the subgroups (all log-rank p < 0.05). The subgroup with worse outcome using the proposed radiomics model was consistently associated with shorter OS, DFS, and liver-specific DFS, with hazard ratios of 1.78 (95 %CI: 1.12-2.83), 1.72 (95 %CI: 1.16-2.54), and 1.59 (95 %CI: 1.10-2.31), respectively. The general performance of this radiomics model outperformed the traditional Clinical Risk Score and Tumor Burden Score in the prognosis prediction after surgery for CRLM.

CONCLUSION

Radiomics features derived from preoperative CT images can reveal the heterogeneity of CRLM and stratify the patients with CRLM into subgroups with significantly different clinical outcomes.

Locally advanced mid/low rectal cancer with synchronous resectable liver metastases: systematic review of the available strategies and outcome

The management of patients with locally advanced mid/low rectal cancer with resectable liver metastases is complex because of the need to combine the optimal treatment of both tumors. This study aims to review the available treatment strategies and compare their outcome, focusing on radiotherapy (RT) and liver-first approach (LFA). A systematic review was performed in PubMed, Embase, and web sources including articles published between 2000 and 02/2023 and reporting mid-/long-term outcomes. Overall, twenty studies were included (n = 1837 patients). Three- and 5-year overall survival (OS) rates were 51-88% and 36-59%. Although several strategies were reported, most patients received RT (1448/1837, 79%; > 85% neoadjuvant). RT reduced the pelvic recurrence risk (5.8 vs. 13.5%, P = 0.005) but did not impact OS. Six studies analyzed LFA (n = 307 patients). LFA had a completion rate similar to the rectum-first approach (RFA, 81% vs. 79%) but the interval strategy-an LFA variant with liver surgery in the interval between radiotherapy and rectal surgery-had a better completion rate than standard LFA (liver surgery/radiotherapy/rectal surgery, 92% vs. 75%, P = 0.011) and RFA (79%, P = 0.048). Across all series, LFA achieved the best survival rates, and in one paper it led to a survival advantage in patients with multiple metastases. In conclusion, different strategies can be adopted, but RT should be included to decrease the pelvic recurrence risk. LFA should be considered, especially in patients with high hepatic tumor burden, and RT before liver surgery (interval strategy) could maximize its completion rate.

Robotic versus open resection for colorectal liver metastases in a "referral centre Hub&Spoke learning program". A multicenter propensity score matching analysis of perioperative outcomes

Background

Surgical resection is still considered the optimal treatment for colorectal liver metastasis (CRLM). Although laparoscopic and robotic surgery demonstrated their reliability especially in referral centers, the comparison between perioperative outcomes of robotic liver resection (RLR) and open (OLR) liver resection are still debated when performed in referral centers for robotic surgery, not dedicated to HPB. Our study aimed to verify the efficacy and safety of perioperative outcomes after RLR and OLR for CRLM in an HUB&Spoke learning program (H&S) between a high volume center for liver surgery and high volume center for robotic surgery.

Methods

We analyzed prospective databases of Pineta Grande Hospital (Castel Volturno) and Robotic Surgical Units (Foligno-Spoleto and Arezzo) from 2011 to 2021. A 1:1 propensity score matching (PSM) was performed according to baseline characteristics of patients, solitary/multiple CRLM, anterolateral/posterosuperior location.

Results

383 patients accepted to be part of the study (268 ORL and 115 RLR). After PSM, 45 patients from each group were included. Conversion rate was 8.89 %. RLR group had a significantly lower blood loss (226 vs. 321 ml; p=0.0001), and fewer major complications (13.33 % vs. 17.78 %; p=0.7722). R0 resection was obtained in 100% of OLR (vs.95.55%, p =0.4944. Hospital stay was 8.8 days in RLR (vs. 15; p=0.0001).Conclusion: H&S represents a safe and effective program to train general surgeons also in Hepatobiliary surgery providing R0 resection rate, blood loss volume and morbidity rate superimposable to referral centers. Furthermore, H&S allow a reduction of health mobility with consequent money saving for patients and institutions.

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.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

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.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography

BACKGROUND

breast cancer (BC) is the world's most prevalent cancer in the female population, with 2.3 million new cases diagnosed worldwide in 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led to significant improvement in patients' survival. The Full-Field Digital Mammograph (FFDM) is considered the gold standard method for the early diagnosis of BC. From several previous studies, it has emerged that breast density (BD) is a risk factor in the development of BC, affecting the periodicity of screening plans present today at an international level.

OBJECTIVE

in this study, the focus is the development of mammographic image processing techniques that allow the extraction of indicators derived from textural patterns of the mammary parenchyma indicative of BD risk factors.

METHODS

a total of 168 patients were enrolled in the internal training and test set while a total of 51 patients were enrolled to compose the external validation cohort. Different Machine Learning (ML) techniques have been employed to classify breasts based on the values of the tissue density. Textural features were extracted only from breast parenchyma with which to train classifiers, thanks to the aid of ML algorithms.

RESULTS

the accuracy of different tested classifiers varied between 74.15% and 93.55%. The best results were reached by a Support Vector Machine (accuracy of 93.55% and a percentage of true positives and negatives equal to TPP = 94.44% and TNP = 92.31%). The best accuracy was not influenced by the choice of the features selection approach. Considering the external validation cohort, the SVM, as the best classifier with the 7 features selected by a wrapper method, showed an accuracy of 0.95, a sensitivity of 0.96, and a specificity of 0.90.

CONCLUSIONS

our preliminary results showed that the Radiomics analysis and ML approach allow us to objectively identify BD.

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

Bile duct tumor thrombus (BDTT) is an uncommon finding in hepatocellular carcinoma (HCC), potentially mimicking cholangiocarcinoma (CCA). Recent studies have suggested that HCC with BDTT could represent a prognostic factor. We report the case of a 47-year-old male patient admitted to the University Hospital of Bari with abdominal pain. Blood tests revealed the presence of an untreated hepatitis B virus infection (HBV), with normal liver function and without jaundice. Abdominal ultrasonography revealed a cirrhotic liver with a segmental dilatation of the third bile duct segment, confirmed by a CT scan and liver MRI, which also identified a heterologous mass. No other focal hepatic lesions were identified. A percutaneous ultrasound-guided needle biopsy was then performed, detecting a moderately differentiated HCC. Finally, the patient underwent a third hepatic segmentectomy, and the histopathological analysis confirmed the endobiliary localization of HCC. Subsequently, the patient experienced a nodular recurrence in the fourth hepatic segment, which was treated with ultrasound-guided percutaneous radiofrequency ablation (RFA). This case shows that HCC with BDTT can mimic different types of tumors. It also indicates the value of an early multidisciplinary patient assessment to obtain an accurate diagnosis of HCC with BDTT, which may have prognostic value that has not been recognized until now.

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics' potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.