Development and external validation of a predictive scoring system associated with metastasis of T1-2 colorectal tumors to lymph nodes

Predictors of early colorectal cancer metastasis to lymph nodes: providing rationale for therapy decisions

With the improvement of national health awareness and the popularization of a series of screening methods, the number of patients with early colorectal cancer is gradually increasing, and accurate prediction of lymph node metastasis of T1 colorectal cancer is the key to determining the optimal therapeutic solutions. Whether patients with T1 colorectal cancer undergoing endoscopic resection require additional surgery and regional lymph node dissection is inconclusive in current guidelines. However, we can be sure that in early colorectal cancer without lymph node metastasis, endoscopic resection alone does not affect the prognosis, and it greatly improves the quality of life and reduces the incidence of surgical complications while preserving organ integrity. Therefore, it is vital to discriminate patients without lymph node metastasis in T1 colorectal cancer, and this requires accurate predictors. This paper briefly explains the significance and shortcomings of traditional pathological factors, then extends and states the new pathological factors, clinical test factors, molecular biomarkers, and the risk assessment models of lymph node metastasis based on artificial intelligence.

Germline polymorphisms of the NOD2 pathway may predict the effectiveness of radioiodine in differentiated thyroid cancer treatment

PURPOSE

Differentiated thyroid cancer (DTC) presents a complex clinical challenge, especially in patients with distant metastases and resistance to standard treatments. This study aimed to investigate the influence of specific genes and their germline single nucleotide polymorphisms (SNPs) linked to both inflammatory processes and other neoplasms on the clinical and pathological characteristics of DTC, particularly their potential impact on radioiodine (RAI) treatment efficacy.

METHODS

This retrospective analysis involved a cohort of 646 patients diagnosed with DTC after thyroidectomy. Study covering 1998-2014, updated in 2023, included 567 women and 79 men (median age: 49; range: 7-83). SNP selection targeted functional significance, while mutational status was assessed by pyrosequencing for comprehensive characterization. Patient genetic profiles were assessed for associations with disease characteristics, RAI response, and cancer pathology.

RESULTS

Significant correlations emerged between certain SNPs and DTC features. Notably, the NOD2 c.802 T > C variant (rs2066842) was identified as a marker distinguishing between papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC). Moreover, the c.802 T allele was associated with an enhanced response to RAI treatment, indicating a more substantial decrease in posttreatment stimulated thyroglobulin (sTg) concentrations. The NFKB1A allele c.126A (rs696) exhibited connections with lower FTC stages and a reduced probability of multifocality.

CONCLUSION

This study explored the molecular mechanisms of particular SNPs, highlighting the role of NOD2 in innate immunity and the stress response, and its potential impact on RAI efficacy. This research underscores the clinical promise of SNP analysis and contributes to personalized treatment strategies for DTC, emphasizing the relevance of genetic factors in cancer progression and treatment outcomes.

Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

Perineural invasion in colorectal cancer: mechanisms of action and clinical relevance

BACKGROUND

In recent years, the significance of the nervous system in the tumor microenvironment has gained increasing attention. The bidirectional communication between nerves and cancer cells plays a critical role in tumor initiation and progression. Perineural invasion (PNI) occurs when tumor cells invade the nerve sheath and/or encircle more than 33% of the nerve circumference. PNI is a common feature in various malignancies and is associated with tumor invasion, metastasis, cancer-related pain, and unfavorable clinical outcomes. The colon and rectum are highly innervated organs, and accumulating studies support PNI as a histopathologic feature of colorectal cancer (CRC). Therefore, it is essential to investigate the role of nerves in CRC and comprehend the mechanisms of PNI to impede tumor progression and improve patient survival.

CONCLUSION

This review elucidates the clinical significance of PNI, summarizes the underlying cellular and molecular mechanisms, introduces various experimental models suitable for studying PNI, and discusses the therapeutic potential of targeting this phenomenon. By delving into the intricate interactions between nerves and tumor cells, we hope this review can provide valuable insights for the future development of CRC treatments.

Management after non-curative endoscopic resection of T1 rectal cancer

Since the introduction of population-based screening, increasing numbers of T1 rectal cancers are detected and removed by local endoscopic resection. Patients can be cured with endoscopic resection alone, but there is a possibility of residual tumor cells remaining after the initial resection. These can be located intraluminally at the resection site or extraluminally in the form of (lymph node) metastases. To decrease the risk of residual cells progressing towards more advanced disease, additional treatment is usually needed. However, with the currently available risk stratification models, it remains challenging to determine who should and should not be further treated after non-curative endoscopic resection. In this review, the different management strategies for patients with non-curatively treated T1 rectal cancers are discussed, along with the available evidence for each strategy and relevant considerations for clinical decision making. Furthermore, we provide practical guidance on the management and surveillance following non-curative endoscopic resection of T1 rectal cancer.

Prediction of disease recurrence or residual disease after primary endoscopic resection of pT1 colorectal cancer-results from a large nationwide Danish study

PURPOSE

Risk assessment of disease recurrence in pT1 colorectal cancer is crucial in order to select the appropriate treatment strategy. The study aimed to develop a prediction model, based on histopathological data, for the probability of disease recurrence and residual disease in patients with pT1 colorectal cancer.

METHODS

The model dataset consisted of 558 patients with pT1 CRC who had undergone endoscopic resection only (n = 339) or endoscopic resection followed by subsequent bowel resection (n = 219). Tissue blocks and slides were retrieved from Pathology Departments from all regions in Denmark. All original slides were evaluated by one experienced gastrointestinal pathologist (TPK). New sections were cut and stained for haematoxylin and eosin (HE) and immunohistochemical markers. Missing values were multiple imputed. A logistic regression model with backward elimination was used to construct the prediction model.

RESULTS

The final prediction model for disease recurrence demonstrated good performance with AUC of 0.75 [95% CI 0.72-0.78], HL chi-squared test of 0.59 and scaled Brier score of 10%. The final prediction model for residual disease demonstrated medium performance with an AUC of 0.68 [0.63-0.72].

CONCLUSION

We developed a prediction model for the probability of disease recurrence in pT1 CRC with good performance and calibration based on histopathological data. Together with lymphatic and venous invasion, an involved resection margin (0 mm) as opposed to a margin of ≤ 1 mm was an independent risk factor for both disease recurrence and residual disease.

Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms

We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning

OBJECTIVES

This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer.

METHODS

In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method.

RESULTS

In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001).

CONCLUSION

The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

KEY POINTS

• Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

Diagnostic performance of synthetic magnetic resonance imaging in the prognostic evaluation of rectal cancer

Background

Numerous factors are related to the prognosis of rectal cancer, including T stage, N stage, metastasis, extramural venous invasion (EMVI), circumferential resection margin (CRM), and tumor differentiation. However, it is still a challenge to precisely evaluate them before therapy; therefore, we investigate whether synthetic magnetic resonance imaging and apparent diffusion coefficient (ADC) values could help predict the prognostic factors of rectal cancer.

Methods

Eighty-seven patients (55 men and 32 women; mean age, 59±11 years) with pathologically confirmed rectal cancer were enrolled. Preoperative quantitative metrics, including T1, T2, proton density (PD), and ADC values, were measured with diffusion-weighted imaging (DWI) acquired by a single-shot echo-planar sequence and synthetic magnetic resonance imaging acquired by a multi-dynamic multi-echo sequence at 3.0 T, in patients with rectal cancer by two radiologists. We evaluated the diagnostic performance of synthetic magnetic resonance imaging using the independent sample t-test or Mann-Whitney U test and receiver operating characteristic (ROC) curve and multivariate logistic regression analyses and compared the area under the ROC curve of quantitative values using the DeLong test.

Results

The T2 and PD values showed a significant reduction among patients with poor differentiation and lymph node metastasis in rectal cancer. The area under the ROC curve values of T2 and PD values for predicting magnetic resonance imaging N stage and differentiation were 0.734, 0.682, and 0.673, 0.686, respectively. Moreover, combining T2 and PD values for magnetic resonance imaging N stage slightly improved the area under the ROC curve value of 0.774 (95% CI, 0.673-0.876). In the present study, the ADC and T1 values were not significant in the differentiation or clinical stage of rectal cancer (RC).

Conclusions

Quantitative T2 and PD values obtained by synthetic magnetic resonance imaging might be used for evaluating prognostic factors of rectal cancer noninvasively. Furthermore, combining T2 and PD values further improved the diagnostic performance of magnetic resonance imaging N staging in rectal cancer. The ADC and T1 values were not significant in the differentiation or clinical stage of RC.

Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer

Background

Accurate prediction of the risk of lymph node metastasis in patients with stage T1 colorectal cancer is crucial for the formulation of treatment plans for additional surgery and lymph node dissection after endoscopic resection. The purpose of this study was to establish a predictive model for evaluating the risk of LNM in patients with stage T1 colorectal cancer.

Methods

The clinicopathological and imaging data of 179 patients with T1 stage colorectal cancer who underwent radical resection of colorectal cancer were collected. LASSO regression and a random forest algorithm were used to screen the important risk factors for LNM, and a multivariate logistic regression equation and dynamic nomogram were constructed. The C index, Calibration curve, and area under the ROC curve were used to evaluate the discriminant and prediction ability of the nomogram. The net reclassification index (NRI), comprehensive discriminant improvement index (IDI), and clinical decision curve (DCA) were compared with traditional ESMO criteria to evaluate the accuracy, net benefit, and clinical practicability of the model.

Results

The probability of lymph node metastasis in patients with T1 colorectal cancer was 11.17% (20/179). Multivariate analysis showed that the independent risk factors for LNM in T1 colorectal cancer were submucosal invasion depth, histological grade, CEA, lymphovascular invasion, and imaging results. The dynamic nomogram model constructed with independent risk factors has good discrimination and prediction capabilities. The C index was 0.914, the corrected C index was 0.890, the area under the ROC curve was 0.914, and the accuracy, sensitivity, and specificity were 93.3, 80.0, and 91.8%, respectively. The NRI, IDI, and DCA show that this model is superior to the ESMO standard.

Conclusion

This study establishes a dynamic nomogram that can effectively predict the risk of lymph node metastasis in patients with stage T1 colorectal cancer, which will provide certain help for the formulation of subsequent treatment plans for patients with stage T1 CRC after endoscopic resection.

Nerve Dependence in Colorectal Cancer

Cancerous invasion of nerves has been reported in a list of malignant tumors as a high-risk pathological feature and marker of poor disease outcome especially in neurotrophic cancers (such as in pancreas and prostate), indicating that although once neglected, nerves could have played a pivotal role in tumorigenesis and cancer progression. In colorectal cancer, perineural invasion, a specific form of tumor-nerve interaction referring to the identification of tumor cells in proximity to the nerve, has been recognized as a strong and independent prognosis predictor; denervation of autonomic nerves and enteric nerves have shown that the existence of these nerves in the gut are accompanied by promoted cancer proliferation, further supporting that nerve is a potential accomplice to shield and nurture tumor cells. However, the precise role of nerve in CRC and the pattern of interaction between CRC cells and nerve has not been unveiled yet. Here we aim to review some basic knowledge of the importance of nerves in CRC and attempt to depict a mechanistic view of tumor-nerve interaction during CRC development.

FOLFIRI (folinic acid, fluorouracil, and irinotecan) increases not efficacy but toxicity compared with single-agent irinotecan as a second-line treatment in metastatic colorectal cancer patients: a randomized clinical trial

Background:

FOLFIRI [irinotecan, folinic acid (CF), and fluorouracil] is considered a standard second-line chemotherapy regimen for patients with metastatic colorectal cancer (mCRC) who failed first-line XELOX/FOLFOX regimens. However, it remains unknown whether fluorouracil is still necessary in this case. This trial was designed to test the superiority of FOLFIRI over single-agent irinotecan as a second-line treatment for patients with mCRC.

Methods:

This randomized clinical trial was conducted in five hospitals in China. From 4 November 2016 to 17 January 2020, patients aged 18 years or older with histologically confirmed unresectable mCRC and who had failed first-line XELOX/FOLFOX regimens were screened and enrolled. Patients were randomized to receive either FOLFIRI or irinotecan. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), and toxicity. Data were analyzed on an intention-to-treat basis.

Results:

A total of 172 patients with mCRC were randomly treated with FOLFIRI (n = 88) or irinotecan (n = 84). The median PFS was 104 and 112 days (3.5 and 3.7 months) in the FOLFIRI and irinotecan groups, respectively [hazard ratio (HR) = 1.084, 95% confidence interval (CI) = 0.7911–1.485; p = 0.6094], and there was also no significant difference in OS and ORR between the two groups. The incidence of the following adverse events (AEs) was significantly higher in the FOLFIRI group than in the irinotecan group: any grade AEs including leucopenia (73.9% versus 55.4%), neutropenia (72.7% versus 56.6%), thrombocytopenia (31.8% versus 18.1%), jaundice (18.2% versus 7.2%), mucositis (40.9% versus 14.5%), vomiting (37.5% versus 21.7%), and fever (19.3% versus 7.2%) and grade 3–4 neutropenia (47.7% versus 21.7%).

Conclusion:

This is the first head-to-head trial showing that single-agent irinotecan yielded PFS, OS, and ORR similar to FOLFIRI, with a more favorable toxicity profile; therefore, it might be a more favorable standard chemotherapy regimen for mCRC patients who failed first-line XELOX/FOLFOX regimens.

Trial registration:

This study is registered with ClinicalTrials.gov, number NCT02935764, registered 17 October 2016, https://clinicaltrials.gov/ct2/show/NCT02935764.

Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer

Background

The overall survival (OS) of patients diagnosed with colon cancer (CC) varied greatly, so did the patients with the same tumor stage. We aimed to design a nomogram that is capable of predicting OS in resected left-sided colon cancers (LSCC) and right-sided colon cancers (RSCC), and thus to stratify patients into different risk groups, respectively.

Methods

Records from a retrospective cohort of 577 patients with complete information were used to construct the nomogram. Univariate and multivariate analyses screened risk factors associated with overall survival. The performance of the nomogram was evaluated with concordance index (c-index), calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits, respectively, which was further compared with the American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification. Risk stratification based on nomogram scores was performed with recursive partitioning analysis.

Results

The LSCC nomogram incorporated carbohydrate antigen 12-5 (CA12-5), age and log odds of positive lymph nodes (LODDS), and RSCC nomogram enrolled tumor stroma percentage (TSP), age and LODDS. Compared with the TNM classification, the LSCC and RSCC nomograms both had a statistically higher C-index (0.837, 95% CI: 0.827-0.846 and 0.780, 95% CI 0.773-0.787, respectively) and more clinical net benefits, respectively. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort.

Conclusions

An original predictive nomogram was constructed and validated for OS in patients with CC after surgery, which had facilitated physicians to appraise the individual survival of postoperative patients accurately and to identify high-risk patients who were in need of more aggressive treatment and follow-up strategies.

Deep learning can predict lymph node status directly from histology in colorectal cancer

BACKGROUND

Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC).

OBJECTIVES

The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM).

METHODS

Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set.

RESULTS

On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage.

CONCLUSION

Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.

Development and validation of a nomogram for further decision of radical surgery in pT1 colorectal cancer after local resection

PURPOSE

The aim of this study was to develop and validate a nomogram to assist physicians making further decisions on the requirement of a radical surgery for T1 colorectal cancer (CRC) after local excision through preoperative prediction of lymph node metastasis (LNM).

METHODS

A total of 141 T1 CRC patients were enrolled from January 2013 to August 2020. The independent predictive parameters were determined in multivariate analyses. The nomogram was constructed based on predictors of LNM and its performance was evaluated with respect to its calibration, discrimination, and decision curve analysis. Internal validation by bootstrapping was performed to verify the applicability of the nomogram.

RESULTS

cN in CT/MRI (N+), histologic type (poorly differentiated, mucinous adenocarcinoma, and signet-ring cell carcinoma), tumor budding (G3), and lymphovascular invasion were identified in the multivariable analysis (p<0.05). The developed nomogram incorporated these four predictors and it yielded good discrimination and calibration, with an area under the curve (AUC) of 0.89 (95% confidence interval [CI]: 0.80-0.97). However, the Japanese guideline yielded an AUC of 0.75 (95% CI: 0.63-0.87). A decision curve analysis showed that the predictive scoring system had a high clinical application value, and the nomogram conferred a greater benefit than the Japanese guideline did (range of threshold within 10%-80%).

CONCLUSIONS

This study proposed a novel predictive model to assist physicians in making treatment decisions regarding additional surgery after local excision.

AKP and GGT level can provide an early prediction of first-line treatment efficacy in colorectal cancer patients with hepatic metastases

Aim: It is important to early evaluate or predict the efficacy to avoid ineffective treatment for most colorectal cancer (CRC) patients with liver metastases. Patients & methods: The medical records of 440 patients with histologically confirmed primary CRC admitted to the Fudan University Shanghai Cancer Center were reviewed. Results: High baseline serum alkaline phosphatase (AKP) and γ-glutamyl transferase (GGT) is associated with worse overall survival. In patients with a high serum AKP and GGT a decreased percentage had high objective response rate and better progression-free survival. Conclusion: Measuring the changes of serum AKP or GGT in CRC patients with hepatic metastases before and after the first cycle of treatment is a convenient, fast and economical way to early predict antitumor treatment efficacy.

Molecular Features of Lymph Node Metastasis in T1/2 Colorectal Cancer from Formalin-Fixed Paraffin-Embedded Archival Specimens

Histological risk factors for lymph node metastasis (LNM) in early-stage colorectal cancers (CRC) have been described, although the predictive utility of these factors varies. Improved LNM risk assessment based on findings in endoscopic colon and rectal excisions is necessary for optimal surgical management of CRC patients with pathologic T1- /T2-staged invasive depth (i.e., tumor not invading beyond the muscularis propria layer); as the current system is overly conservative, and results in many unnecessary radical surgeries. To identify molecular features in early CRC with elevated LNM potential, we carried out proteomic and gene expression profiling to compare T1 lymph node (LN) negative with T1/2 LN positive CRC tumors from formalin-fixed paraffin-embedded (FFPE) specimens. Using a data-independent acquisition mass spectrometry workflow, we detected over 7400 proteins and quantified over 4400 in all 21 specimens. Proteins from tumors with LN metastasis were enriched with effectors of epithelial-mesenchymal transition (EMT) and gene expression profiling confirmed activation of key transcription factors, SNAI1 and ZEB1, as well as a reduction in E-cadherin expression. Toward an implementation pathway, we investigated immunohistochemistry assays targeting four EMT-related proteins. While MS could reliably discern twofold protein abundance changes, we found the semiquantitative nature of IHC scoring limited confirmation of this degree of protein expression difference. This study demonstrated that EMT effectors are associated with locoregional metastasis in T1/T2 CRC and could be used to augment metastatic risk assessment, although further developments are required to enable routine implementation.