Can medical imaging identify the histopathological growth patterns of liver metastases?

Microscopic Growth Pattern of Metastatic Colorectal Carcinomas, Morphological Findings of the Non-Neoplastic Liver, and Their Relationship With Prognosis and Survival

Introduction. Various clinicopathological, radiological, and molecular parameters are predictive of prognosis in patients with colorectal carcinoma and distant organ metastases continue to have a significant place among them. Recent studies reveal that not only the presence of metastases but also the histopathological growth pattern of the metastatic tumor significantly affects prognosis. This study aimed to investigate the prognostic significance of the histopathological growth patterns of metastatic tumors, the morphological findings in the peritumoral non-neoplastic liver, and its relationship with survival in patients who have metastatic colorectal carcinoma. Materials and Method. Hematoxylin and eosin-stained slides of the tumors were re-examined in terms of histopathological diagnosis, growth pattern, presence and degree of peritumoral lymphocytic infiltration, steatosis, cholestasis, and peritumoral ductular reaction in the non-neoplastic liver. Results. In terms of histopathological growth patterns, 24 (47%) tumors showed replacement, 19 (37%) showed desmoplastic and 8 (16%) showed pushing growth pattern. In terms of total survival, there was a significant difference (P = .011) between desmoplastic and replacement growth patterns, and the survival period was shorter in patients with replacement growth patterns. Conclusion. Recent studies show that histopathological growth patterns in metastatic liver tumors may be a promising prognostic and predictive parameter. It is important to include this parameter in the pathology reports as it does not require additional equipment for evaluation in routine pathology practice, does not bring additional costs, or takes a long time to evaluate. This feature can be evaluated standardly by every pathologist.

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

Preoperative chemotherapy in upfront resectable colorectal liver metastases: New elements for an old dilemma?

The use of preoperative or "neoadjuvant" chemotherapy (NAC) has long been controversial for resectable colorectal liver metastases (CRLM). The European Society of Medical Oncology (ESMO) 2023 guidelines on metastatic colorectal cancer (CRC) indicate a combination of surgical/technical and oncologic/prognostic criteria as the two determinants for allocating patients to NAC or upfront hepatectomy. However, surgical and technical criteria have evolved, and oncologic prognostic criteria date from the pre-modern chemotherapy era and lack prospective validation. The traditional literature is interpreted as not supporting the use of NAC because several studies fail to demonstrate a benefit in overall survival (OS) compared to upfront surgery; however, OS may not be the most appropriate endpoint to consider. Moreover, the commonly quoted studies against NAC contain many limitations that may explain why NAC failed to demonstrate its value. The query of the recent literature focused primarily on other aspects than OS, such as surgical technique, the impact of side effects of chemotherapy, the histological growth pattern of metastases, or the detection of circulating tumor DNA, shows data that support a more widespread use of NAC. These should prompt a critical reappraisal of the use of NAC, leading to a more precise selection of patients who could benefit from it.

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.

Histopathological growth pattern of liver metastases as an independent marker of metastatic behavior in different primary cancers

Surgical resection can lead to prolonged survival in patients with isolated liver metastases (LM) from various primary cancers. However, there are currently no validated predictive markers to discriminate between these oligo/argometastatic patients, who will benefit from surgery, and those with diffuse metastatic behavior in whom surgery will be futile. To evaluate whether the tumor microenvironment, or histopathological growth pattern (HGP), of LM reflects the type of metastatic progression independently of the origin of the primary cancer, we analyzed a combined series of patients who underwent surgery for colorectal LM (N=263) or non-colorectal LM (N=66). HGPs of LM were scored in each patient to distinguish between desmoplastic HGP (all LM showing a complete encapsulated pattern) and non-desmoplastic HGP (at least one LM with some infiltrating-replacement component). In the entire series, 5-year overall and progression-free survival were, 44.5% and 15.5%, respectively, with no significant differences between colorectal and non-colorectal LM. In patients with desmoplastic HGP, 5-year overall and progression-free survival were 57% and 32%, respectively, as compared to 41% and 12%, respectively, in patients with non-desmoplastic-HGP (p=0.03 and 0.005). Irrespective of cancer origin and compared to traditional risk factors, desmoplastic HGP was the most significant predictor for better post-operative overall survival (adjusted HR: 0.62; 95% CI: [0.49-0.97]; p=0.035) and progression-free survival (adjusted HR: 0.61; 95% CI: [0.42-0.87], p=0.006). This suggests that the HGP of LM may represent an accurate marker that reflects the mode of metastatic behavior, independently of primary cancer type.

Association between primary tumor characteristics and histopathological growth pattern of liver metastases in colorectal cancer

INTRODUCTION

The microarchitecture of liver metastases (LMs), or histopathological growth pattern (HGP), has been demonstrated to be a significant prognostic factor in patients undergoing resection of colorectal liver metastases (CRLMs). Currently, however, HGP can be only determined on the operative specimen. Therefore, the development of new tools to predict the HGP of CRLMs before surgery and to understand the mechanisms that drive these patterns is important for improving individualization of therapeutic management. In this study, we analyzed data from a retrospective series of patients who underwent surgery for CRLMs to compare primary tumor characteristics, including markers of local aggressiveness and migratory capacity, and HGP of liver metastases.

METHODS

Data from a retrospective series of 167 patients who underwent curative-intent resection of CRLMs and in whom pathological samples from both primary tumor and liver metastases were available were reviewed. At the primary tumor level, KRAS mutational status, grade of differentiation, and tumor budding were assessed. HGP was scored in each resected CRLM, according to consensus guidelines, and classified as desmoplastic (dHGP) or non-desmoplastic (non-dHGP). Associations between primary tumor characteristics and HGP of CRLMs were evaluated using a binary logistic regression model. Overall survival and disease-free survival were evaluated using Kaplan-Meier and multivariable Cox regression analyses.

RESULTS

CRLMs were classified as dHGP in 36% of the patients and as non-dHGP in 64%. Higher rates of moderately or poorly differentiated primary tumors were observed in the non-dHGP CRLM group (80%), as compared with the dHGP group (60%) (OR = 3.6; 95%CI: 1.6-7.05; p = 0.001). Higher rates of tumor budding were observed in the non-dHGP CRLM group, with a median tumor budding value of 4 as compared with 2.5 in the dHGP group (p = 0.042). In the entire series, 5-year overall and disease-free survival were 43% and 32.5%, respectively. The non-dHGP CRLM group had worse post-hepatectomy survival, with 5-year overall and disease-free survival of 32.2% and 24.6%, respectively, as compared with 60.8% and 45.9%, respectively, for the dHGP group (p = 0.02).

CONCLUSION

Colorectal tumors with moderate or poor differentiation and those with high tumor budding are more frequently associated with CRLMs with a non-dHGP. This suggests that primary tumor characteristics of local aggressiveness and migratory capacity could preferentially promote the development of CRLMs with an infiltrating pattern and that these parameters should be considered as part of new scores for predicting HGP before surgery. This finding may stimulate new lines of research for more individualized therapeutic decision in patients with CRLM candidate to surgery.

Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab

PURPOSE

Early evaluation of the efficacy of first-line chemotherapy combined with bevacizumab in patients with colorectal cancer liver metastasis (CRLM) remains challenging. This study used 2-month post-chemotherapy spectral computed tomography (CT) to predict the overall survival (OS) and response of CRLM patients with bevacizumab-containing therapy.

METHOD

This retrospective analysis was performed in 104 patients with pathologically confirmed CRLM between April 2017 and October 2021. Patients were treated with 5-fluorouracil, leucovorin, oxaliplatin or irinotecan with bevacizumab. Portal venous phase spectral CT was performed on the target liver lesion within 2 months of commencing chemotherapy to demonstrate the iodine concentration (IoD) of the target liver lesion. The patients were classified as responders (R +) or non-responders (R -) according to the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 at 6 months. Multivariate analysis was performed to determine the relationships of the spectral CT parameters, tumor markers, morphology of target lesions with OS and response. The differences in portal venous phase spectral CT parameters between the R + and R - groups were analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of spectral CT parameters.

RESULTS

Of the 104 patients (mean age ± standard deviation: 57.73 years ± 12.56; 60 men) evaluated, 28 (26.9%) were classified as R + . Cox multivariate analysis identified the iodine concentration (hazard ratio [HR]: 1.238; 95% confidence interval [95% CI]: 1.089-1.408; P < 0.001), baseline tumor longest diameter (BLD) (HR: 1.022; 95% CI: 1.005-1.038, P = 0.010), higher baseline CEA (HR: 1.670; 95% CI: 1.016-2.745, P = 0.043), K-RAS mutation (HR: 2.027; 95% CI: 1.192-3.449; P = 0.009), and metachronous liver metastasis (HR: 1.877; 95% CI: 1.179-2.988; P = 0.008) as independent risk factors for patient OS. Logistic multivariate analysis identified the IoD (Odds Ratio [OR]: 2.243; 95% CI: 1.405-4.098; P = 0.002) and clinical N stage of the primary tumor (OR: 4.998; 95% CI: 1.210-25.345; P = 0.035) as independent predictor of R + . Using IoD cutoff values of 4.75 (100ug/cm³) the area under the ROC curve was 0.916, sensitivity and specificity were 80.3% and 96.4%, respectively.

CONCLUSIONS

Spectral CT IoD can predict the OS and response of patients with CRLM after 2 months of treatment with bevacizumab-containing therapy.

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.

Histopathological Growth Pattern in Colorectal Liver Metastasis and The Tumor Immune Microenvironment

Almost half of all patients with colorectal cancer present with or eventually develop metastasis, most frequently in the liver. Understanding the histopathological growth patterns and tumor immune microenvironment of colorectal liver metastases may help determine treatment strategies and assess prognosis. A literature search was conducted to gather information on cancer biology, histopathological growth patterns, and the tumor immune microenvironment in colorectal liver metastases, including their mechanisms and their impact on clinical outcomes. A first consensus on histopathological growth patterns emerged in 2017, identifying five growth patterns. Later studies found benefits from a two-tier system, desmoplastic and non-desmoplastic, incorporated into the updated 2022 consensus. Furthermore, the tumor immune microenvironment shows additional characteristic features with relevance to cancer biology. This includes density of T-cells (CD8+), expression of claudin-2, presence of vessel co-option versus angiogenesis, as well as several other factors. The relation between histopathological growth patterns and the tumor immune microenvironment delineates distinct subtypes of cancer biology. The distinct subtypes are found to correlate with risk of metastasis or relapse, and hence to clinical outcome and long-term survival in each patient. In order to optimize personalized and precision therapy for patients with colorectal liver metastases, further investigation into the mechanisms of cancer biology and their translational aspects to novel treatment targets is warranted.

Histopathological growth patterns of liver metastasis: updated consensus guidelines for pattern scoring, perspectives and recent mechanistic insights

The first consensus guidelines for scoring the histopathological growth patterns (HGPs) of liver metastases were established in 2017. Since then, numerous studies have applied these guidelines, have further substantiated the potential clinical value of the HGPs in patients with liver metastases from various tumour types and are starting to shed light on the biology of the distinct HGPs. In the present guidelines, we give an overview of these studies, discuss novel strategies for predicting the HGPs of liver metastases, such as deep-learning algorithms for whole-slide histopathology images and medical imaging, and highlight liver metastasis animal models that exhibit features of the different HGPs. Based on a pooled analysis of large cohorts of patients with liver-metastatic colorectal cancer, we propose a new cut-off to categorise patients according to the HGPs. An up-to-date standard method for HGP assessment within liver metastases is also presented with the aim of incorporating HGPs into the decision-making processes surrounding the treatment of patients with liver-metastatic cancer. Finally, we propose hypotheses on the cellular and molecular mechanisms that drive the biology of the different HGPs, opening some exciting preclinical and clinical research perspectives.

CT, MRI, and radiomics studies of liver metastasis histopathological growth patterns: an up-to-date review

The histopathological growth patterns (HGPs) of liver metastases (LMs) are independently associated with the long-term prognosis of the primary tumor, with different HGPs predicting different patient outcomes and clinical treatment decisions. Non-invasive imaging biomarkers for stratification of HGPs are beneficial for treatment monitoring, evaluation of efficacy, and prognosis prediction of LMs. This review describes the state of research regarding computed tomography (CT), magnetic resonance imaging (MRI), and radiomics imaging biomarkers for LM-HGPs; discusses the advantages of CT, MRI, and radiomics for classification of LM-HGPs; and provides a reference for the stratification of LM-HGPs. Finally, the difficulties and deficiencies of CT, MRI, and radiomics in LM-HGP research are summarized along with the proposed directions for future research.

A Comprehensive Prognostic Model for Colorectal Cancer Liver Metastasis Recurrence After Neoadjuvant Chemotherapy

Background

For patients with colorectal cancer liver metastases (CRLMs), it is important to stratify patients according to the risk of recurrence. This study aimed to validate the predictive value of some clinical, imaging, and pathology biomarkers and develop an operational prognostic model for patients with CRLMs with neoadjuvant chemotherapy (NACT) before the liver resection.

Methods

Patients with CRLMs accompanied with primary lesion and liver metastases lesion resection were enrolled into this study. A nomogram based on independent risk factors was identified by Kaplan–Meier analysis and multivariate Cox proportional hazard analysis. The predictive ability was evaluated by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Calibration plot were also used to explore the consistency between prediction and reality.

Results

A total of 118 patients were enrolled into the study. Multivariable Cox analysis found that histopathological growth patterns (HGPs) [Hazard Rate (HR) = 2.130], radiology response (stable disease vs. partial response, HR = 2.207; progressive disease vs. partial response, HR = 3.824), lymph node status (HR = 1.442), and age (HR = 0.576) were independent risk factors for disease-free survival (DFS) (p < 0.05). Corresponding nomogram was constructed on the basis of the above factors, demonstrating that scores ranging from 5 to 11 presented better prognosis than the scores of 0–4 (median DFS = 14.3 vs. 4.9 months, p < 0.0001). The area under ROC curves of the model for 1-, 2-, and 3-year DFS were 0.754, 0.705, and 0.666, respectively, and DCA confirmed that the risk model showed more clinical benefits than clinical risk score. Calibration plot for the probability of DFS at 1 or 3 years verified an optimal agreement between prediction and actual observation. In the course of our research, compared with pure NACT, a higher proportion of desmoplastic HGP (dHGP) was detected in patients treated with NACT plus cetuximab (p = 0.030), and the use of cetuximab was an independent factor for decreased replacement HGP (rHGP) and increased dHGP (p = 0.049).

Conclusion

Our model is concise, comprehensive, and high efficient, which may contribute to better predicting the prognosis of patients with CRLMs with NACT before the liver resection. In addition, we observed an unbalanced distribution of HGPs as well.

Growth Pattern of Hepatic Metastasis as a Prognostic Index Reflecting Liver Metastasis-Associated Survival in Breast Cancer Liver Metastasis

Breast cancer with liver metastasis (BCLM) frequently cause hepatic failure owing to extensive liver metastasis compared to other cancers; however, there are no clinicopathologic or radiologic parameters for estimating BCLM prognosis. We analyzed the relationship between radiologic and clinicopathologic characteristics with survival outcomes in BCLM. During 2009-2019, baseline and final abdomen computed tomography or liver magnetic resonance imaging of BCLM patients were reviewed. Liver metastasis patterns were classified as oligometastasis (≤3 metastatic lesions), non-confluent or confluent mass formation, infiltration, and pseudocirrhosis. Thirty-one surgical or biopsy specimens for liver metastasis were immunostained for L1 adhesion molecule (L1CAM), Yes-associated protein 1/Transcriptional co-activator with PDZ-binding motif (YAP/TAZ), and β1-integrin. Out of 156 patients, 77 initially had oligometastasis, 58 had nonconfluent mass formation, 14 had confluent mass formation, and 7 had infiltrative liver metastasis. Confluent or infiltrative liver metastasis showed inferior liver metastasis-associated survival (LMOS) compared to others (p = 0.001). Positive staining for L1CAM and YAP/TAZ was associated with inferior survival, and YAP/TAZ was related to final liver metastasis. Initial hepatic metastasis was associated with LMOS, especially confluent mass formation, and infiltrative liver metastasis pattern was associated with poor survival. Positive staining for YAP/TAZ and L1CAM was associated with inferior LMOS, and YAP/TAZ was related to final liver metastasis.

Histopathological growth patterns of resected non-colorectal, non-neuroendocrine liver metastases: a retrospective multicenter studyss

BACKGROUND

Distinct Histopathological Growth Patterns can be identified in liver metastases from melanoma, breast and colorectal cancers. For each of these distinct liver metastasis types the HGP has proven a biomarker for survival after partial hepatectomy, with the desmoplastic type marking favourable prognosis. Whether HGPs can be considered a pan-cancer phenomenon remains unknown. This study therefore evaluates the presence of HGPs and their prognostic value across non-colorectal non-neuroendocrine liver metastases.

METHODS

A retrospective multicentre cohort study was performed in patients who underwent curative intent resection of non-colorectal non-neuroendocrine liver metastasis. HGPs were assessed on Haematoxylin and Eosin slides according to consensus guidelines and classified as desmoplastic or non-desmoplastic. Overall- and recurrence-free survival were evaluated using Kaplan-Meier and multivariable Cox regression analysis.

RESULTS

In total, 132 patients with liver metastasis from 25 different tumour types were eligible for analysis, of which 26 (20%) had a desmoplastic HGP. Five-year OS and RFS (95%CI) were 53% (36-78%) versus 40% (30-53%), and 33% (19-61%) versus 15% (9-27%) for patients with desmoplastic compared to non-desmoplastic metastases, respectively (p = 0.031 & p = 0.004). On multivariable analysis (adjusted HR [95%CI]) a desmoplastic HGP was prognostic for both OS (0.46 [0.25-0.86]) and RFS (0.38 [0.21-0.69]).

CONCLUSIONS

This study demonstrates that HGPs apply to liver metastases across a wide variety of primary tumour origins. They hold a prognostic value in these cases, suggesting that HGPs could represent a pan-cancer biomarker for survival after surgical resection of liver metastases.

Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.

Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study

Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with pure HGPs [i.e. 100% desmoplastic (dHGP) or 100% replacement (rHGP)] and a CT-scan who were surgically treated at the Erasmus MC between 2003-2015 were included retrospectively. Each lesion was segmented by three clinicians and a convolutional neural network (CNN). A prediction model was created using 564 radiomics features and a combination of machine learning approaches by training on the clinician's and testing on the unseen CNN segmentations. The intra-class correlation coefficient (ICC) was used to select features robust to segmentation variations; ComBat was used to harmonize for acquisition variations. Evaluation was performed through a 100 × random-split cross-validation. The study included 93 CRLM in 76 patients (48% dHGP; 52% rHGP). Despite substantial differences between the segmentations of the three clinicians and the CNN, the radiomics model had a mean area under the curve of 0.69. ICC-based feature selection or ComBat yielded no improvement. Concluding, the combination of a CNN for segmentation and radiomics for classification has potential for automatically distinguishing dHGPs from rHGP, and is robust to segmentation and acquisition variations. Pending further optimization, including extension to mixed HGPs, our model may serve as a preoperative addition to postoperative HGP assessment, enabling further exploitation of HGPs as a biomarker.

Different Forms of Tumor Vascularization and Their Clinical Implications Focusing on Vessel Co-option in Colorectal Cancer Liver Metastases

In modern anti-cancer therapy of metastatic colorectal cancer (mCRC) the anti-angiogenic treatment targeting sprouting angiogenesis is firmly established for more than a decade. However, its clinical benefits still remain limited. As liver metastases (LM) represent the most common metastatic site of colorectal cancer and affect approximately one-quarter of the patients diagnosed with this malignancy, its treatment is an essential aspect for patients' prognosis. Especially in the perioperative setting, the application of anti-angiogenic drugs represents a therapeutic option that may be used in case of high-risk or borderline resectable colorectal cancer liver metastases (CRCLM) in order to achieve secondary resectability. Regarding CRCLM, one reason for the limitations of anti-angiogenic treatment may be represented by vessel co-option (VCO), which is an alternative mechanism of blood supply that differs fundamentally from the well-known sprouting angiogenesis and occurs in a significant fraction of CRCLM. In this scenario, tumor cells hijack pre-existing mature vessels of the host organ independently from stimulating new vessels formation. This represents an escape mechanism from common anti-angiogenic anti-cancer treatments, as they primarily target the main trigger of sprouting angiogenesis, the vascular endothelial growth factor A. Moreover, the mechanism of blood supply in CRCLM can be deduced from their phenotypic histopathological growth pattern (HGP). For that, a specific guideline has already been implemented. These HGP vary not only regarding their blood supply, but also concerning their tumor microenvironment (TME), as notable differences in immune cell infiltration and desmoplastic reaction surrounding the CRCLM can be observed. The latter actually serves as one of the central criteria for the classification of the HGP. Regarding the clinically relevant effects of the HGP, it is still a topic of research whether the VCO-subgroup of CRCLM results in an impaired treatment response to anti-angiogenic treatment when compared to an angiogenic subgroup. However, it is well-proved, that VCO in CRCLM generally relates to an inferior survival compared to the angiogenic subgroup. Altogether the different types of blood supply result in a relevant influence on the patients' prognosis. This reinforces the need of an extended understanding of the underlying mechanisms of VCO in CRCLM with the aim to generate more comprehensive approaches which can target tumor vessels alternatively or even other components of the TME. This review aims to augment the current state of knowledge on VCO in CRCLM and other tumor entities and its impact on anti-angiogenic anti-cancer therapy.

Infiltrative Tumor Borders in Colorectal Liver Metastasis: Should We Enlarge Margin Size?

BACKGROUND

Surgery is the only potentially curative treatment for colorectal cancer liver metastases (CRLMs). Despite an improvement in results following resection, recurrence rates remain high. Many histopathological features have been reported as prognostic factors. Infiltrative borders are known to be associated with worse prognosis; however, margin size has never been evaluated together with the type of tumor border. In the present study, we analyzed the prognosis of patients with resected CRLM according to tumor growth pattern (TGP) and whether a larger margin size would bring any prognostic benefit.

PATIENTS AND METHODS

Medical records from a prospective database of 645 patients who underwent hepatic resection for CRLM between January 2004 and December 2019 at a single center were reviewed, and 266 patients were included in the analytic cohort. TGP (pushing or infiltrative) was evaluated regarding the impact in overall and disease-free survival. The impact of margin size (≤ or > 1 cm) on survival and hepatic recurrence according to TGP was also evaluated.

RESULTS

TGP was defined as infiltrative in 182 cases (68.4%) and pushing in 84 patients (31.6%). Patients with infiltrative-type border presented worse overall survival and disease-free survival, as well as higher intrahepatic recurrence (p < 0.05). Larger margin size did not impact the prognosis of patients with infiltrative borders.

CONCLUSIONS

Patients with infiltrative-type border present worse prognosis and higher intrahepatic recurrence. Larger margin size (> 1 cm) does not change the prognosis in patients with infiltrative border, showing that tumor biology is the most important factor for survival.

Clinico-metabolic characterization improves the prognostic value of histological growth patterns in patients undergoing surgery for colorectal liver metastases

BACKGROUND AND OBJECTIVES

The histological growth pattern (HGP) represents a strong prognostic factor in patients undergoing surgery for colorectal liver metastases (CRLM). We evaluated whether the combination of HGP with clinico-metabolic parameters could improve its prognostic value.

METHODS

In a series of 108 patients undergoing resection of CRLM, the HGP of CRLM was scored according to international guidelines. Baseline clinico-metabolic clinical status was evaluated using a metabolic-Clinical Risk Score (mCRS), combining traditional Memorial Sloan Kettering-CRS parameters with the tumor-to-liver glucose uptake ratio as measured with ¹⁸ Fluorodeoxyglucose/positron emission tomography.

RESULTS

In patients with desmoplastic HGP (DHGP) CRLM (20% of all patients), 5- and 10-years overall survival (OS) and disease free survival (DFS) were 66% and 43% and 37% and 24.5%, as compared with 35% and 21% and 11% and 11% in the non-DHGP group (p = 0.07 and 0.054). Among DHGP patients, those with a low-risk mCRS had improved postoperative outcomes, 5- and 10-years OS and DFS reaching 83.3% and 62.5% and 50% and 33%, as compared with 18% and 0% and 0% and 0% in high-risk mCRS patients (p = 0.007 and 0.003). In contrast, mCRS did not influence postoperative survivals in non-DHGP patients.

CONCLUSIONS

Combining the clinico-metabolic characteristics with the HGP may improve prognostication in patients undergoing surgery for CRLM.

The Tumor Microenvironment in Liver Metastases from Colorectal Carcinoma in the Context of the Histologic Growth Patterns

Colorectal carcinoma (CRC) is the third most common cancer. Likewise, it is a disease that has a long survival if it is prematurely detected. However, more than 50% of patients will develop metastases, mainly in the liver (LM-CRC), throughout the evolution of their disease, which accounts for most CRC-related deaths. Treatment it is certainly a controversial issue, since it has not been shown to increase overall survival in the adjuvant setting, although it does improve disease free survival (DFS). Moreover, current chemotherapy combinations are administered based on data extrapolated from primary tumors (PT), not considering that LM-CRC present a very particular tumor microenvironment that can radically condition the effectiveness of treatments designed for a PT. The liver has a particular histology and microenvironment that can determine tumor growth and response to treatments: double blood supply, vascularization through fenestrated sinusoids and the presence of different mesenchymal cell types, among other particularities. Likewise, the liver presents a peculiar immune response against tumor cells, a fact that correlates with the poor response to immunotherapy. All these aspects will be addressed in this review, putting them in the context of the histological growth patterns of LM-CRC, a particular pathologic feature with both prognostic and predictive repercussions.