How Reliable Is CT Scan in Staging Right Colon Cancer?

Evaluation of colon cancer prognostic factors by CT and MRI: an up-to-date review

Colorectal cancer (CRC) is a significant global health concern. Prognostication of CRC traditionally relies on the Union for International Cancer Control (UICC) and American Joint Committee on Cancer (AJCC) TNM staging classifications, yet clinical outcomes often vary independently of stage. Despite similarities, rectal and colon cancers are distinct in their diagnostic methodologies and treatments, with MRI and CT scans primarily used for staging rectal and colon cancers, respectively. This paper examines the challenges in accurately assessing prognostic factors of colon cancer such as primary tumor extramural extension, retroperitoneal surgical margin (RSM) involvement, extramural vessel invasion (EMVI), and lymph node metastases through preoperative CT and MRI. It highlights the importance of these factors in risk stratification, treatment decisions, and surgical planning for colon cancer patients. Advancements in imaging techniques are crucial for improving clinical management and optimizing patient outcomes, underscoring the necessity for ongoing research to refine diagnostic methods and incorporate novel findings into practice.

The prognostic role of circulating tumour DNA detected prior to clinical diagnosis of colorectal cancer in the HUNT study

BACKGROUND

Today, the prognostic tools available at the time of diagnosis in colorectal cancer (CRC) are limited. Better prognostic tools are a prerequisite for personalised treatment. This study aimed to investigate whether circulating tumour DNA (ctDNA) markers found in plasma before clinical diagnosis of CRC could contribute to the prediction of poor prognosis.

METHODS

This observational cohort study included patients diagnosed with CRC stage I-III within 24 months following participation in the Trøndelag Health Study (n = 85). Known methylated ctDNA biomarkers of CRC were analysed by PCR in plasma. Outcomes were overall survival (OS), recurrence-free survival (RFS) and poor prognosis (PP). Candidate clinical and methylated ctDNA predictors of the outcomes were identified by Cox regression analyses.

RESULTS

Methylated GRIA4 (HR 1.96 (1.06-3.63)), RARB (HR 9.48 (3.00-30.00)), SLC8A1 (HR 1.97 (1.03-3.77)), VIM (HR 2.95 (1.22-7.14)) and WNT5A (HR 5.83 (2.33-14.56)) were independent predictors of OS, methylated RARB (HR 9.67 (2.54-36.81)), SDC2 (HR 3.38 (1.07-10.66)), SLC8A1 (HR 2.93 (1.01-8.51)) and WNT5A (HR 6.95 (1.81-26.68)) were independent predictors of RFS and methylated RARB (HR 6.11 (1.69-22.18)), SDC2 (HR 2.79 (1.20-6.49)) and WNT5A (HR 5.57 (3.04-15.26)) were independent predictors of PP (p < 0.05).

CONCLUSIONS

Prediagnostic ctDNA markers are promising contributors to predicting poor prognosis in CRC, potentially becoming one of the tools guiding more personalised treatment.

A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer

To develop a clinical-radiomics nomogram based on spectral CT multi-parameter images for predicting lymph node metastasis in colorectal cancer. A total of 76 patients with colorectal cancer and 156 lymph nodes were included. The clinical data of the patients were collected, including gender, age, tumor location and size, preoperative tumor markers, etc. Three sets of conventional images in the arterial, venous, and delayed phases were obtained, and six sets of spectral images were reconstructed using the arterial phase spectral data, including virtual monoenergetic images (40 keV, 70 keV, 100 keV), iodine density maps, iodine no water maps, and virtual non-contrast images. Radiomics features of lymph nodes were extracted from the above images, respectively. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select features. A clinical model was constructed based on age and carcinoembryonic antigen (CEA) levels. The radiomics features selected were used to generate a composed radiomics signature (Com-RS). A nomogram was developed using age, CEA, and the Com-RS. The models' prediction efficiency, calibration, and clinical application value were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, respectively. The nomogram outperforms the clinical model and the Com-RS (AUC = 0.879, 0.824). It is well calibrated and has great clinical application value. This study developed a clinical-radiomics nomogram based on spectral CT multi-parameter images, which can be used as an effective tool for preoperative personalized prediction of lymph node metastasis in colorectal cancer.

A Concept for Preoperative and Intraoperative Molecular Imaging and Detection for Assessing Extent of Disease of Solid Tumors

The authors propose a concept of "systems engineering," the approach to assessing the extent of diseased tissue (EODT) in solid tumors. We modeled the proof of this concept based on our clinical experience with colorectal carcinoma (CRC) and gastrinoma that included short and long-term survival data of CRC patients. This concept, applicable to various solid tumors, combines resources from surgery, nuclear medicine, radiology, pathology, and oncology needed for preoperative and intraoperative assessments of a patient's EODT. The concept begins with a patient presenting with biopsy-proven cancer. An appropriate preferential locator (PL) is a molecule that preferentially binds to a cancer-related molecular target (i.e., tumor marker) lacking in non-malignant tissue and is the essential element. Detecting the PL after an intravenous injection requires the PL labeling with an appropriate tracer radionuclide, a fluoroprobe, or both. Preoperative imaging of the tracer's signal requires molecular imaging modalities alone or in combination with computerized tomography (CT). These include positron emission tomography (PET), PET/CT, single-photon emission computed tomography (SPECT), SPECT/CT for preoperative imaging, gamma cameras for intraoperative imaging, and gamma-detecting probes for precise localization. Similarly, fluorescent-labeled PLs require appropriate cameras and probes. This approach provides the surgeon with real-time information needed for R0 resection.

Role of artificial intelligence in colorectal cancer

The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.

Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

The ELECLA trial: A multicentre randomised control trial on outcomes of neoadjuvant treatment on locally advanced colon cancer

BACKGROUND

Colon cancer (CC) is a public health concern with increasing incidence in younger populations. Treatment for locally advanced CC (LACC) involves oncological surgery and adjuvant chemotherapy (AC) to reduce recurrence and improve overall survival (OS). Neoadjuvant chemotherapy (NAC) is a novel approach for the treatment of LACC, and research is underway to explore its potential benefit in terms of survival. This trial will assess the efficacy of NAC in LACC.

METHODS

This is a multicentre randomised, parallel-group, open label controlled clinical trial. Participants will be selected based on homogenous inclusion criteria and randomly assigned to two treatment groups: NAC, surgery, and AC or surgery followed by AC. The primary aim of this study is to evaluate the 2-year progression-free survival (PFS), with secondary outcomes including 5-year PFS, 2- and 5-year OS, toxicity, radiological and pathological response, morbidity, and mortality.

DISCUSSION

The results of this study will determine whether NAC induces a clinical and histological tumour response in patients with CCLA and if this treatment sequence improves survival without increasing morbidity and mortality.

REGISTRATION NUMBER

NCT04188158.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer

INTRODUCTION

Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT.

METHODS

In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC.

RESULTS

A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively.

CONCLUSION

A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

The diagnostic accuracy of local staging in colon cancer based on computed tomography (CT): evaluating the role of extramural venous invasion and tumour deposits

PURPOSE

The shift from adjuvant to neoadjuvant treatment in colon cancer demands the radiological selection of patients for systemic therapy. The aim of this study was to evaluate the accuracy of the CT-based TNM stage and high-risk features, including extramural venous invasion (EMVI) and tumour deposits, in the identification of patients with histopathological advanced disease, currently considered for neoadjuvant treatment (T3-4 disease).

METHODS

All consecutive patients surgically treated for non-metastatic colon cancer between January 2018 and January 2020 in a referral centre for colorectal cancer were identified retrospectively. All tumours were staged on CT according to the TNM classification system. Additionally, the presence of EMVI and tumour deposits on CT was evaluated. The histopathological TNM classification was used as reference standard.

RESULTS

A total of 176 patients were included. Histopathological T3-4 colon cancer was present in 85.0% of the patients with CT-detected T3-4 disease. Histopathological T3-4 colon cancer was present in 96.4% of the patients with CT-detected T3-4 colon cancer in the presence of both CT-detected EMVI and CT-detected tumour deposits. Histopathological T0-2 colon cancer was present in 50.8% of the patients with CT-detected T0-2 disease, and in 32.4% of the patients without CT-detected EMVI and tumour deposits.

CONCLUSION

The diagnostic accuracy of CT-based staging was comparable with previous studies. The presence of high-risk features on CT increased the probability of histopathological T3-4 colon cancer. However, a substantial part of the patients without CT-detected EMVI and tumour deposits was diagnosed with histopathological T3-4 disease. Hence, more accurate selection criteria are required to correctly identify patients with locally advanced disease.

Prognostic impact of extramural venous invasion detected by contrast-enhanced CT colonography in colon cancer

BACKGROUND

The impact of computed tomography (CT)-detected extramural venous invasion on the recurrence of colon cancer is not fully understood. The aim of this study was to investigate the clinical significance of extramural venous invasion diagnosed before surgery by contrast-enhanced CT colonography using three-dimensional multiplanar reconstruction images.

METHODS

Patients with colon cancer staged greater than or equal to T2 and/or stage I-III who underwent contrast-enhanced CT colonography between 2013 and 2018 at the National Cancer Center Hospital in Japan were retrospectively investigated for CT-detected extramural venous invasion. Inter-observer agreement for the detection of CT-detected extramural venous invasion was evaluated and Kaplan-Meier survival curves were plotted for recurrence-free survival using CT-TNM staging and CT-detected extramural venous invasion. Preoperative clinical variables were analysed using Cox regression for recurrence-free survival.

RESULTS

Out of 922 eligible patients, 544 cases were analysed (50 (9.2 per cent) were diagnosed as positive for CT-detected extramural venous invasion and 494 (90.8 per cent) were diagnosed as negative for CT-detected extramural venous invasion). The inter-observer agreement for CT-detected extramural venous invasion had a κ coefficient of 0.830. The group positive for CT-detected extramural venous invasion had a median follow-up of 62.1 months, whereas the group negative for CT-detected extramural venous invasion had a median follow-up of 60.7 months. When CT-TNM stage was stratified according to CT-detected extramural venous invasion status, CT-T3 N(-)extramural venous invasion(+) had a poor prognosis compared with CT-T3 N(-)extramural venous invasion(-) and CT-stage I (5-year recurrence-free survival of 50.6 versus 89.3 and 90.1 per cent respectively; P < 0.001). In CT-stage III, the group positive for CT-detected extramural venous invasion also had a poor prognosis compared with the group negative for CT-detected extramural venous invasion (5-year recurrence-free survival of 52.0 versus 78.5 per cent respectively; P = 0.003). Multivariable analysis revealed that recurrence was associated with CT-T4 (HR 3.10, 95 per cent c.i. 1.85 to 5.20; P < 0.001) and CT-detected extramural venous invasion (HR 3.08, 95 per cent c.i. 1.90 to 5.00; P < 0.001).

CONCLUSION

CT-detected extramural venous invasion was found to be an independent predictor of recurrence and could be used in combination with preoperative TNM staging to identify patients at high risk of recurrence.

Small arteriole sign: an imaging feature for staging T4a colon cancer

OBJECTIVES

By analyzing the distribution of existing and newly proposed staging imaging features in pT1-3 and pT4a tumors, we searched for a salient feature and validated its diagnostic performance.

METHODS

Preoperative multiphase contrast-enhanced CT images of the training cohort were retrospectively collected at three centers from January 2016 to December 2017. We used the chi-square test to analyze the distribution of several stage-related imaging features in pT1-3 and pT4a tumors, including small arteriole sign (SAS), outer edge of the intestine, tumor invasion range, and peritumoral adipose tissue. Preoperative multiphase contrast-enhanced CT images of the validation cohort were retrospectively collected at Beijing Cancer Hospital from January 2018 to December 2018. The diagnostic performance of the selected imaging feature, including accuracy, sensitivity, and specificity, was validated and compared with the conventional clinical tumor stage (cT) by the McNemar test.

RESULTS

In the training cohort, a total of 268 patients were enrolled, and only SAS was significantly different between pT1-3 and pT4a tumors. The accuracy, sensitivity, and specificity of the SAS and conventional cT in differentiating T1-3 and T4a tumors were 94.4%, 81.6%, and 97.3% and 53.7%, 32.7%, and 58.4%, respectively (all p < 0.001). In the validation cohort, a total of 135 patients were collected. The accuracy, sensitivity, and specificity of the SAS and the conventional cT were 93.3%, 76.2%, and 96.5% and 62.2%, 38.1%, and 66.7%, respectively (p < 0.001, p = 0.021, p < 0.001).

CONCLUSION

Small arteriole sign positivity, an indirect imaging feature of serosa invasion, may improve the accuracy of identifying T4a colon cancer.

CLINICAL RELEVANCE STATEMENT

Small arteriole sign helps to distinguish T1-3 and T4a colon cancer and further improves the accuracy of preoperative CT staging of colon cancer.

KEY POINTS

• The accuracy of preoperative CT staging of colon cancer is not ideal, especially for T4a tumors. • Small arteriole sign (SAS) is a newly defined imaging feature that shows the appearance of tumor-supplying arterioles at the site where they penetrate the intestine wall. • SAS is an indirect imaging marker of tumor invasion into the serosa with a great value in distinguishing between T1-3 and T4a colon cancer.

Diagnostic Accuracy of Abdominal CT for Locally Advanced Colon Tumors: Can We Really Entrust Certain Decisions to the Reliability of CT?

Many different options of neoadjuvant treatments for advanced colon cancer are emerging. An accurate preoperative staging is crucial to select the most appropriate treatment option. A retrospective study was carried out on a national series of operated patients with T4 tumors. Considering the anatomo-pathological analysis of the surgical specimen as the gold standard, a diagnostic accuracy study was carried out on the variables T and N staging and the presence of peritoneal metastases (M1c). The parameters calculated were sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios, as well as the overall accuracy. A total of 50 centers participated in the study in which 1950 patients were analyzed. The sensitivity of CT for correct staging of T4 colon tumors was 57%. Regarding N staging, the overall accuracy was 63%, with a sensitivity of 64% and a specificity of 62%; however, the positive and negative likelihood ratios were 1.7 and 0.58, respectively. For the diagnosis of peritoneal metastases, the accuracy was 94.8%, with a sensitivity of 40% and specificity of 98%; in the case of peritoneal metastases, the positive and negative likelihood ratios were 24.4 and 0.61, respectively. The diagnostic accuracy of CT in the setting of advanced colon cancer still has some shortcomings for accurate diagnosis of stage T4, correct classification of lymph nodes, and preoperative detection of peritoneal metastases.

Preoperative Local Staging of Colon Cancer by CT: Radiological Staging Criteria Based on Membrane Anatomy and Visceral Adipose Tissue

BACKGROUND

Accuracy of preoperative T staging for colon cancer remains disappointing.

OBJECTIVE

This study aimed to propose specially designed radiological staging criteria based on membrane anatomy and visceral adipose tissue and compare the staging performance with the routinely used method.

DESIGN

This is a prospective observational study.

SETTING

This study was conducted at a high-volume colorectal center.

PARTICIPANTS

Consecutive patients with colonoscopy-proven colon carcinoma referred for clinical staging and elective resection were enrolled.

INTERVENTION

The preoperative CT data were separately reviewed by 2 teams of radiologists for assigning T-stage categories (T1-2, T3, or T4) using the routine staging method or the newly proposed radiological criteria.

MEASURES

Diagnostic performance for T staging was compared between the 2 criteria.

RESULTS

Between October 2019 and August 2020, 190 patients were included. Compared with pathological results, T stage was correctly determined in 113 of 190 patients (59.5%) with the conventional CT criteria. With the newly developed criteria, 160 patients (84.2%) were found to be correctly staged. Accuracies between the 2 criteria significantly differed ( p < 0.001). For T1-2 staging, there were no significant differences between the sensitivities of conventional and new criteria (57.1% vs 61.9%; p = 0.990) or between their specificities (95.3% vs 98.2%; p = 0.131). However, for T3 and T4 staging, the newly developed CT criteria exhibited significantly higher sensitivity (T3: 85.2% vs 57.4%; p < 0.001; T4: 90.7% vs 64.8%; p < 0.001) and specificity (T3: 82.7% vs 64%; p = 0.006; T4: 89.7% vs 69.1%; p < 0.001) than the conventional criteria. Moreover, the new criteria (area under the curve = 0.902) performed significantly better than the conventional criteria (area under the curve = 0.670; p < 0.001), for identifying the T4-stage tumor.

LIMITATIONS

The limitations are that it is a single-center study and there was no external validation.

CONCLUSIONS

The specially designed radiological criteria can offer more accurate T staging than the routine method in colon cancer. See Video Abstract at http://links.lww.com/DCR/B992 .

PREDICCIN DE LA MORTALIDAD A DAS POSTERIORES A LA PRIMERA CIRUGA EN PACIENTES CON CNCER DE COLON OBSTRUCTIVO DEL LADO IZQUIERDO

ANTECEDENTES:Se cree que la resección aguda para el carcinoma de colon obstructivo del lado izquierdo está asociada con un mayor riesgo de mortalidad que un enfoque puente a la cirugía que utiliza un estoma de descompresión o un stent metálico autoexpandible, pero faltan modelos de predicción.OBJETIVO:Determinar la influencia de la estrategia de tratamiento sobre la mortalidad dentro de los 90 días desde la primera intervención utilizando un modelo de predicción en pacientes que presentan carcinoma de colon obstructivo del lado izquierdo.DISEÑO:Un estudio de cohorte multicéntrico nacional, utilizando datos de una auditoría nacional prospectiva.ENTORNO CLINICO:El estudio se realizó en 75 hospitales holandeses.PACIENTES:Se incluyeron los pacientes que se sometieron a una resección con intención curativa de un carcinoma de colon obstructivo del lado izquierdo entre 2009 y 2016.INTERVENCIONES:La primera intervención fue resección aguda, puente a cirugía con stent metálico autoexpandible o puente a cirugía con estoma descompresor.PRINCIPALES MEDIDAS DE VALORACIÓN:La principal medida de resultado fue la mortalidad a los 90 días después de la primera intervención. Los factores de riesgo se identificaron mediante análisis logístico multivariable. Posteriormente se desarrolló un modelo de riesgo.RESULTADOS:En total se incluyeron 2395 pacientes, siendo la primera intervención resección aguda en 1848 (77%) pacientes, estoma como puente a la cirugía en 332 (14%) pacientes y stent como puente a la cirugía en 215 (9%) pacientes. En general, 152 pacientes (6,3%) fallecieron dentro de los 90 días posteriores a la primera intervención. Un estoma de descompresión se asoció de forma independiente con un menor riesgo de mortalidad a los 90 días (HR: 0,27, IC: 0,094-0,62). Otros predictores independientes de mortalidad fueron la edad, la clasificación ASA, la ubicación del tumor y los niveles índice de creatinina sérica y proteína C reactiva. El modelo de riesgo construido tuvo un área bajo la curva de 0,84 (IC: 0,81-0,87).LIMITACIONES:Solo se incluyeron pacientes que se sometieron a resección quirúrgica.CONCLUSIONES:La estrategia de tratamiento tuvo un impacto significativo en la mortalidad a los 90 días. Un estoma descompresor reduce considerablemente el riesgo de mortalidad, especialmente en pacientes mayores y frágiles. Se desarrolló un modelo de riesgo, que necesita una mayor validación externa. Consulte Video Resumen en http://links.lww.com/DCR/B992 . (Traducción-Dr. Ingrid Melo ).

Current Controversies in the Management of Locally Advanced Colon Cancer

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Correlation between lymph node size on pathology and metastatic disease in right-sided colon cancer: A retrospective review

BACKGROUND

Identification of positive lymph nodes in colon cancer can significantly impact treatment. Few studies have examined the role of lymph node size in staging and prognosis. This study evaluated the relationship between lymph node size and lymph node metastases in right-sided colon cancer.

METHODS

Retrospective chart review was performed for patients undergoing colectomy for right-sided colon cancer from 2015 to 2020 across a single multi-hospital health system. Patients under age 18 or who did not have invasive adenocarcinoma upon pathological examination were excluded. Primary endpoints assessed lymph node size and lymph node metastases. 572 patients were stratified by lymph node size; lymph nodes ≥5 mm (n = 308) were characterized as enlarged.

RESULTS

All surgical specimens examined had adequate number of lymph nodes for staging. 33.9% of all specimens examined contained lymph node metastases. Patients with enlarged lymph nodes were significantly more likely to have lymph node metastases than those with normal-sized lymph nodes (p < 0.001). Enlarged lymph nodes were associated with advanced nodal staging.

CONCLUSIONS

Patients with enlarged nodes were significantly more likely to have lymph node metastases than those with normal-sized lymph nodes. Further research to analyze these enlarged lymph nodes on radiologic imaging is warranted to determine the role of radiographic assessment of lymph node size during pre-operative staging.

A prospective study of diagnostic accuracy of multidisciplinary team and radiology reporting of preoperative colorectal cancer local staging

INTRODUCTION

The aim of this study was to correlate and assess diagnostic accuracy of preoperative staging at multidisciplinary team meeting (MDT) against the original radiology reports and pathological staging in colorectal cancer patients.

METHODS

A prospective observational study was conducted at two institutions. Patients with histologically proven colorectal cancer and available preoperative imaging were included. Preoperative tumor and nodal staging (cT and cN) as determined by the MDT and the radiology report (computed tomography [CT] and/or magnetic resonance imaging [MRI]) were recorded. Kappa statistics were used to assess agreement between MDT and the radiology report for cN staging in colon cancer, cT and cN in rectal cancer, and tumor regression grade (TRG) in patients with rectal cancer who received neoadjuvant therapy. Pathological report after surgery served as the reference standard for local staging, and AUROC curves were constructed to compare diagnostic accuracy of the MDT and radiology report.

RESULTS

A total of 481 patients were included. Agreement between MDT and radiology report for cN stage was good in colon cancer (k = .756, Confidence Interval (CI) 95% .686-.826). Agreement for cT and cN and in rectal cancer was very good (kw = .825, CI 95% .758-.892) and good (kw = .792, CI 95% .709-.875), respectively. In the rectal cancer group that received neoadjuvant therapy, agreement on TRG was very good (kw = .919, CI 95% .846-.993). AUROC curves using pathological staging indicated no difference in diagnostic accuracy between MDT and radiology reports for either colon or rectal cancer.

CONCLUSION

Preoperative colorectal cancer local staging was consistent between specialist MDT review and original radiology reports, with no significant differences in diagnostic accuracy identified.

Operative and Pathological Factors in Right-Sided Colon Cancers: How Can We Improve the Outcomes?

INTRODUCTION

Though the tumour-node-metastasis staging classification is the standard approach to risk stratification in patients with colorectal cancer, several other important variables including the presence of extramural venous invasion (EMVI), the tumour mismatch repair status, as well as surgical technique and its influence on lymph node yield all have an impact on long-term survival. This study aims to review both the impact of the type of operation on lymph node yield: complete mesocolic excision (CME) versus right hemicolectomy, and the impact of EMVI and microsatellite instability in predicting overall survival in patients undergoing a right hemicolectomy for colon cancer.

METHODS

Data of all patients who underwent an elective or emergency right hemicolectomy with curative intent for colon cancer between January 2013 and June 2022 (inclusive) was collected for this single-centre retrospective study. Kaplan-Meier survival curves were calculated using the Statistical Package for the Social Sciences (SPSS version 28, IBM Corp., Armonk, NY) software, and the log-rank (Mantel-Cox) test was used to compare survival distribution between different groups.

RESULTS

A total of 421 patients underwent a right hemicolectomy for colon cancer with curative intent during the study period. EMVI was present in 173 (41%) tumours. Survival analysis showed significantly reduced cancer-related survival in patients with EMVI-positive tumours (p < 0.001), with five-year survival rates of 70% in EMVI-positive groups versus 96% in EMVI-negative groups. Subgroup analysis showed a significant difference in survival between node-positive and node-negative tumours in cancers found to have EMVI (p < 0.001). Mean lymph node yield was significantly higher in the CME group versus the standard right hemicolectomy group (p < 0.001). We found no significant difference in survival between patients with microsatellite instability-high (MSI-H) tumours and microsatellite stable (MSS) tumours (p = 0.432).

CONCLUSION

Consideration of tumour biology and adopting the optimum surgical technique are factors that may influence long-term survival in patients with colorectal cancer. Extramural venous invasion is an important prognostic indicator of adverse outcomes in patients with right-sided colon cancer. Our study demonstrates a reduction in survival in patients with EMVI-positive tumours when undertaking subgroup analysis by the presence or absence of nodal disease. Further research needs to be undertaken to compare the relative efficacy of neoadjuvant versus adjuvant chemotherapy in right-sided cancers known to be EMVI-positive as some patients will fail to have adjuvant chemotherapy due to postoperative complications, thereby delaying recovery and missing the optimum window for treatment.

Prognostic value of elastic lamina staining in patients with stage III colon cancer

OBJECTIVE

The objectives of this study were to analyze the difference between the preoperative radiological and postoperative pathological stages of colorectal cancer (CRC) and explore the feasibility of elastic lamina invasion (ELI) as a prognostic marker for patients with stage III colon cancer.

METHODS

A total of 105 consecutive patients underwent radical surgery (R0 resection) for stage III colon cancer at the Cancer Hospital of China Medical University between January 2015 and December 2017. Clinicopathological features, including radiological stage and elastic lamina staining, were analyzed for prognostic significance in stage III colon cancer.

RESULTS

A total of 105 patients with stage III colon cancer who met the criteria and had complete data available were included. The median follow-up period of survivors was 41 months. During the follow-up period, 33 (31.4%) patients experienced recurrence after radical resection, and the 3-year disease-free survival (DFS) rate was 64.8%. The consistency between preoperative radiological and postoperative pathological staging was poor (κ = 0.232, P < 0.001). The accuracy of ≤ T2 stage diagnoses was 97.1% (102/105), that of T3 stage was 60.9% (64/105), that of T4a stage was 68.6% (72/105) and that of T4b stage was 91.4% (96/105). The DFS rate of T3 ELI (+) patients was significantly lower than that of both T3 ELI (-) patients (P = 0.000) and pT4a patients (P = 0.013). The DFS rate of T3 ELI (-) patients was significantly higher than that of pT4b patients (P=0.018). T3 ELI (+) (HR (Hazard ratio), 8.444 [95% CI, 1.736-41.067]; P = 0.008), T4b (HR, 57.727[95% CI, 5.547-600.754]; P = 0.001), N2 stage (HR, 10.629 [95% CI, 3.858-29.286]; P < 0.001), stage III (HR, 0.136 [95% CI, 0.31-0.589]; P = 0.008) and perineural invasion (PNI) (HR, 8.393 [95% CI, 2.094-33.637]; P = 0.003) were independent risk factors for postoperative recurrence of stage III colon cancer.

CONCLUSIONS

The consistency between preoperative radiological and postoperative pathological staging was poor, especially for tumors located in the ascending colon and descending colon. Elastic lamina staining is expected to become a stratified indicator of recurrence risk for patients with stage III colon cancer and a guide for individualized adjuvant chemotherapy, thus improving patient prognosis.

Extramural venous invasion and depth of extramural invasion on preoperative CT as prognostic imaging biomarkers in patients with locally advanced ascending colon cancer

PURPOSE

This study evaluates the prognostic significance of EMVI and DEMI on preoperative CT in patients with ascending colon cancer.

METHODS

This retrospective study included consecutive patients with T3 ascending colon cancer from January 2012 to December 2016 in a tertiary center. Two radiologists independently reviewed EMVI, DEMI, and nodal status on preoperative CT. We assessed the association of age, sex, mucinous adenocarcinoma, EMVI, and DEMI with metastasis on preoperative CT using univariable and multivariable analysis. We also compared disease-free survival (DFS) with and without variables (age, sex, mucinous adenocarcinoma, EMVI, DEMI and adjuvant chemotherapy) using Cox's proportional hazards models. We assessed interobserver agreements on imaging features using the Cohen's weighted kappa.

RESULTS

Of 237 patients [107 men; mean (standard deviation) age, 66 (13) years], 24 had metastases on preoperative CT. Positive EMVI was associated with metastasis (odds ratio 16.9; P < 0.001) on multivariable analysis. Of 194 patients [83 men; 65 (13) years] included for DFS analysis, recurrence was observed in 31 (16%) with median follow-up of 53 months. Positive EMVI [hazard ratio (HR) 4.8; P < 0.001] and DEMI > 5 mm (HR 5.5; P < 0.001) were associated with worse DFS. Interobserver agreements were good (kappa = 0.64-0.67).

CONCLUSION

Positive EMVI and DEMI > 5 mm on preoperative CT were associated with a worse T3 ascending colon cancer prognosis. Thus, these CT findings could be used as imaging biomarkers for T3 ascending colon cancer risk stratification.

The role of neoadjuvant chemotherapy in patients with locally advanced colon cancer: A systematic review and meta-analysis

Background

Controversy persists about neoadjuvant chemotherapy (NAC) within the field of locally advanced colon cancer (LACC). The purpose of this study was to assess the existing and latest literature with high quality to determine the role of NAC in various aspects.

Methods

A comprehensive literature search of the PubMed, Embase, Web of Science, and the Cochrane Library databases was conducted from inception to April 2022. Review Manager 5.3 was applied for meta-analyses with a random-effects model whenever possible.

Results

Overall, 8 studies were included in this systematic review and meta-analysis, comprising 4 randomized controlled trials (RCTs) and 4 retrospective studies involving 40,136 participants. The 3-year overall survival (OS) (HR: 0.90, 95% CI: 0.66-1.23, P = 0.51) and 5-year OS (HR: 0.89, 95% CI: 0.53-1.03, P = 0.53) were comparable between two groups. Mortality in 30 days was found less frequent in the NAC group (OR: 0.43, 95% CI: 0.20-0.91, P = 0.03), whereas no significant differences were detected concerning other perioperative complications, R0 resection, or adverse events. In terms of subgroup analyses for RCTs, less anastomotic leak (OR: 0.51, 95% CI: 0.31-0.86, P = 0.01) and higher R0 resection rate (OR: 2.35, 95% CI: 1.04-5.32, P = 0.04) were observed in the NAC group.

Conclusions

NAC is safe and feasible for patients with LACC, but no significant survival benefit could be demonstrated. The application of NAC still needs to be prudent until significant evidence supporting the oncological outcomes is presented.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier (CRD42022333306).

Utility of dual-layer spectral-detector CT imaging for predicting pathological tumor stages and histologic grades of colorectal adenocarcinoma

Objectives

To assess the utility of Dual-layer spectral-detector CT (DLCT) in predicting the pT stage and histologic grade for colorectal adenocarcinoma (CRAC).

Methods

A total of 131 patients (mean 62.7 ± 12.9 years; 72 female, 59 male) with pathologically confirmed CRAC (35 pT1-2, 61 pT3, and 35 pT4; 32 high grade and 99 low grade), who received dual-phase DLCT were enrolled in this retrospective study. Normalized iodine concentration (NIC), slope of the spectral HU curve (λHU), and effective atomic number (Eff-Z) were measured for each lesion by two radiologists independently. Intraobserver reliability and interobserver agreement were assessed. The above values were compared between three pT-stage and two histologic-grade groups. The correlation between the pT stages and above values were assessed. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic efficacy.

Results

Intra-class correlation coefficients were ranged from 0.856 to 0.983 for all measurements. Eff-Z [7.21(0.09) vs 7.31 (0.10) vs 7.35 (0.19)], NIC_AP [0.11 (0.05) vs 0.15 (0.08) vs 0.15 (0.08)], NIC_VP [0.27 (0.06) vs 0.34 (0.11) vs 0.35 (0.12)], λHU_AP [1.20 (0.45) vs 1.93 (1.18) vs 2.37 (0.91)], and λHU_VP [2.07 (0.68) vs 2.35 (0.62) vs 3.09 (1.07)] were significantly different among pT stage groups (all P<0.001) and exhibited a positive correlation with pT stages (r= 0.503, 0.455, 0.394, 0.512, 0.376, respectively, all P<0.001). Eff-Z [7.37 (0.10) vs 7.28 (0.08)], NIC_AP[0.20 (0.10) vs 0.13 (0.08)], NIC_VP[0.35 (0.07) vs 0.31 (0.11)], and λHU_AP [2.59 (1.11) vs 1.63 (0.75)] in the high-grade group were markedly higher than those in the low-grade group (all P<0.05). For discriminating the advanced- from early-stage CARC, the AUCs of Eff-Z, NIC_AP, NIC_VP, λHU_AP, and λHU_VP were 0.83, 0.80, 0.79, 0.86, and 0.68, respectively (all P<0.001). For discriminating the high- from low-grade CARC, the AUCs of Eff-Z, NIC_AP, NIC_VP, and λHU_AP were 0.81, 0.81, 0.64, and 0.81, respectively (all P<0.05).

Conclusions

The quantitative parameters derived from DLCT may provide new markers for assessing pT stages and histologic differentiation in patients with CRAC.

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

Background

Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer.

Methods

A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis.

Results

Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725).

Conclusion

AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce.

Trial registration

PROSPERO CRD42020218004.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08773-w.

Proper surgical extent for clinical Stage I right colon cancer

Purpose:

Pre-operative evaluation identifying clinical-stage affects the decision regarding the extent of surgical resection in right colon cancer. This study was designed to predict a proper surgical resection through the prognosis of clinical Stage I right colon cancer.

Patients and Methods:

We included patients who were diagnosed with clinical and pathological Stage I right-sided colon cancer, including appendiceal, caecal, ascending, hepatic flexure and proximal transverse colon cancer, between August 2010 and December 2016 in two tertiary teaching hospitals. Patients who underwent open surgeries were excluded because laparoscopic surgery is the initial approach for colorectal cancer in our institutions.

Results:

Eighty patients with clinical Stage I and 104 patients with pathological Stage I were included in the study. The biopsy reports showed that the tumour size was larger in the clinical Stage I group than in the pathological Stage I group (3.4 vs. 2.3 cm, P < 0.001). Further, the clinical Stage I group had some pathological Stage III cases (positive lymph nodes, P = 0.023). The clinical Stage I group had a higher rate of distant metastases (P = 0.046) and a lower rate of overall (P = 0.031) and cancer-specific survival (P = 0.021) than the pathological Stage I group. Compared to pathological Stage II included in the period, some of the survival curves were located below the pathological Stage II, but there was no statistical difference.

Conclusion:

The study results show that even clinical Stage I cases, radical resection should be considered in accordance with T3 and T4 tumours.

Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis

PURPOSE

Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies.

METHODOLOGY

Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify eligible studies published between January 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms or radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool.

RESULTS

In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n = 20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist's interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95 %CI, 0.810-0.980) for urological malignancy, and the lowest AUC 0.798 (95 %CI, 0.744-0.852) for colorectal malignancy.

CONCLUSION

Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist's assessment. Deep learning models may further improve on this, but data remain limited.

Impact of visceral adipose tissue on the accuracy of T-staging by CT in colon cancer

OBJECTIVE

Accuracy of preoperative T-staging for colon cancer remains disappointing and may potentially influenced by patients' individual characteristics including visceral adipose tissue (VAT). We sought to clarify the impact of VAT on the accuracy of T-staging by CT.

METHODS

This study of 216 consecutive patients who underwent elective surgery was conducted in a single cancer center, to control other potentially confounding factors. Patients were divided into accurate- and mis-staging groups according to the comparison between preoperative CT-defined (cT) and postoperative pathologic T-stages (pT). Patients' individual characteristics, including CT-based VAT at L2/L3 level, age, sex, body mass index (BMI), tumor location, present of bowel obstruction and pathologic subtype, were compared between the two groups. Association between VAT and mis-staging was assessed using multivariate logistic regression to adjust for confounders.

RESULTS

Of the 216 patients, 84 (39%) were mis-staged by CT. The mean VAT in accurate-staging group was significantly higher than that in mis-staging group (146.8 ± 66.1 cm² vs 98.1 ± 44.7 cm², P < 0.001), with an optimal cutoff point of 122 cm² for predicting mis-staging. After partial adjustment, a lower VAT (< 122 cm², P < 0.001) and proximal location of tumor (P = 0.004) were independent factors associated with higher probability of mis-staging. Compared to VAT ≥ 122 cm² as the reference, VAT < 122 cm² exhibited an odds ratio of 2.701 (95% confidence intervals [CI], 1.618-3.907) for the probability of mis-staging.

CONCLUSION

A lower-VAT is associated with an increased probability of inaccurate clinical T-staging in colon cancer.

Potential image-based criteria of neoadjuvant chemotherapy for colon cancer: multireaders' diagnostic performance

PURPOSE

The objective was to assess which image-based criteria can be best accurately determined at MDCT and which results in least overtreatment.

MATERIALS AND METHODS

A total of 110 consecutive patients, who underwent curative surgery for colon cancer, were included in this retrospective study. Five radiologists independently assessed the longitudinal diameter of cancer as well as T- and N-categories. The five image-based criteria (T3cd/T4, T3/T4, T3/T4 or N+, T3cd/T4 or N2, and T3/T4 with ≥ 4 cm) were evaluated in terms of diagnostic accuracy, interreader agreement, and overtreatment risk using pooled receiver-operating curve and Fleiss kappa analyses. Pathologic high-risk stage II or III was used as a reference standard for assessment of overtreatment risk.

RESULTS

The diagnostic accuracy of multireaders was in the acceptable range (pooled area under curve (AUC): 0.751-0.829). T3/T4 showed the highest AUC (0.829) in terms of diagnostic accuracy. T3/T4 with ≥ 4 cm showed the highest kappa value (κ = 0.695) followed by T3/T4 (κ = 0.623), indicating substantial agreement. The other three criteria revealed moderate agreement (κ = 0.558-0.577). In terms of overtreatment ratio, T3cd/T4 and T3cd/T4 or N2 showed relatively lower ratios (T3cd/T4, 2.2%; T3cd/T4 or N2, 2.9%), whereas T3/T4 and T3/T4 or N+ revealed higher ratios (T3/T4, 8.7%; T3/T4 or N+, 9.5%).

CONCLUSIONS

T3/T4 was the best criterion in terms of diagnostic accuracy. However, in terms of interreader agreement and overtreatment risk, T3/T4 with ≥ 4 cm and T3cd/T4 were better as potential image-based criteria of neoadjuvant chemotherapy for colon cancer.

Performance comparison between MRI and CT for local staging of sigmoid and descending colon cancer

PURPOSE

To compare the diagnostic performance of MRI and CT for local staging of sigmoid and descending colon cancer, with pathological results as the reference standard.

METHOD

This retrospective study included 116 patients with sigmoid or descending colon cancer who underwent both MRI and CT before surgery. MRI and CT images were separately reviewed by two independent and blinded radiologists to assess the following features: T-stage, presence of extramural extension (T3-4 disease), lymph node metastases (N+), and extramural vascular invasion (EMVI+). Diagnostic performance with sensitivity and specificity for detecting positive status (T3-4, N+ or EMVI+) were assessed using receiver-operating-characteristic (ROC) curve, and compared between MRI and CT.

RESULTS

MRI achieved correct T-stage in 81 of 116 patients (69.8 %) while CT in 66 (56.9 %). For detecting T3-4 disease, MRI showed better performance than CT with area under the curve (AUC) of 0.888 versus 0.712 (P =  0.002) and specificity of 81.82 % versus 54.6 % (P =  0.011). No significance was found in sensitivity between two modalities (89.2 % versus 83.1 %, P =  0.302). For detecting N+ disease, performance of MRI and CT were similar (AUC, 0.670 versus 0.650, P =  0.412). For detecting EMVI+, MRI showed better performance than CT (AUC, 0.780 versus 0.575, P =  0.012) with significantly higher sensitivity (68.6 % versus 40.0 %, P =  0.031) and similar specificity (both are 84.3 %).

CONCLUSIONS

MRI may offer more superior diagnostic performance than CT for detecting T3-4 disease and EMVI, thereby supporting its alternative application to CT in local staging of colon cancer.