Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective

Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study

RATIONALE AND OBJECTIVES

To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.

MATERIALS AND METHODS

This retrospective study involved 286 cancer patients confirmed by histopathology from center 1 (Training set) and 66 patients from center 2 (External test set). Radiomics features were extracted from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, whereas DL features were obtained using four models: MobileNet-V3-large, Inception-V3, ResNet50, and VGG16. These DL radiomics (DLR) features were then combined to construct a machine learning model. The Shapley additive interpretation (SHAP) tool was utilized to investigate the interpretability of the model. We evaluated and compared the diagnostic performance of senior and junior radiologists, with and without the aid of the optimal DLR model. Kaplan-Meier survival curve was used to analyze the prognosis of patients.

RESULTS

The DLR model outperforms individual DL models and the radiomics model. The MobileNet-V3-large combination radiomics signature demonstrated the best performance, achieving an AUC of 0.878 on the Training set and 0.752 on the External test set. Compared to the traditional radiomics model, the AUC for the Training set increased by 0.094 and by 0.051 for the External test set. This model facilitated improved diagnostic performance among both junior and senior radiologists. Specifically, the AUC values for junior and senior radiologists increased by 0.162 and 0.232, respectively, on the Training set; and by 0.096 and 0.113, respectively, on the External test set. The DLR model demonstrated strong performance in risk stratification for disease-free survival.

CONCLUSION

The DLR model developed from multiparametric MRI can effectively distinguish cancer LN metastasis and enhance radiologists' diagnostic performance.

State-of-the-art performance of deep learning methods for pre-operative radiologic staging of colorectal cancer lymph node metastasis: a scoping review

OBJECTIVES

To assess the current state-of-the-art in deep learning methods applied to pre-operative radiologic staging of colorectal cancer lymph node metastasis. Specifically, by evaluating the data, methodology and validation of existing work, as well as the current use of explainable AI in this fast-moving domain.

DESIGN

Scoping review.

DATA SOURCES

Academic databases MEDLINE, Embase, Scopus, IEEE Xplore, Web of Science and Google Scholar were searched with a date range of 1 January 2018 to 1 February 2024.

ELIGIBILITY CRITERIA

Includes any English language research articles or conference papers published since 2018 which have applied deep learning methods for feature extraction and classification of colorectal cancer lymph nodes on pre-operative radiologic imaging.

DATA EXTRACTION AND SYNTHESIS

Key results and characteristics for each included study were extracted using a shared template. A narrative synthesis was then conducted to qualitatively integrate and interpret these findings.

RESULTS

This scoping review covers 13 studies which met the inclusion criteria. The deep learning methods had an area under the curve score of 0.856 (0.796 to 0.916) for patient-level lymph node diagnosis and 0.904 (0.841 to 0.967) for individual lymph node assessment, given with a 95% confidence interval. Most studies have fundamental limitations including unrepresentative data, inadequate methodology, poor model validation and limited explainability techniques.

CONCLUSIONS

Deep learning methods have demonstrated the potential for accurately diagnosing colorectal cancer lymph nodes using pre-operative radiologic imaging. However, several methodological and validation flaws such as selection bias and lack of external validation make it difficult to trust the results. This review has uncovered a research gap for robust, representative and explainable deep learning methods that are end-to-end from automatic lymph node detection to the diagnosis of lymph node metastasis.

MRI for Rectal Cancer: Updates and Controversies-AJR Expert Panel Narrative Review

Rectal MRI is a critical tool in the care of patients with rectal cancer, having established roles for primary staging, restaging, and surveillance. The comprehensive diagnostic and prognostic information provided by MRI helps to optimize treatment decision-making. However, challenges persist in the standardization and interpretation of rectal MRI, particularly in the context of rapidly evolving treatment paradigms, including growing acceptance of nonoperative management. In this AJR Expert Panel Narrative Review, we address recent advances and key areas of contention relating to the use of MRI for rectal cancer. Our objectives include: to discuss concepts regarding anatomic localization of rectal tumors; review the evolving rectal cancer treatment paradigm and implications for MRI assessment; discuss updates and controversies regarding rectal MRI for locoregional staging, restaging, and surveillance; review current rectal MRI acquisition protocols; and discuss challenges in homogenizing and optimizing acquisition parameters.

The Role of Lateral Pelvic Lymph Node Dissection in Middle and Lower Rectal Cancer (Stage II or III): A Literature Review

Lateral lymph node dissection and its inclusion in the treatment of rectal cancer is a controversial issue, with great differences, especially between Eastern and Western countries. Studies try to highlight the superiority of resection of these lymph nodes compared to simple mesorectal resection in terms of local recurrence of the disease, the overall survival of patients, and additional postoperative complications. In this study, the modern literature was reviewed, with the ultimate goal of clarifying the exact importance of lateral lymph node dissection, in terms of oncological outcome in patients with cancer of the middle and lower rectum, by studying the involvement of this lymph node dispersion in terms of local recurrence and overall survival of patients with rectal cancer. This review was carried out using electronic databases, including PubMed, Embase, and MEDLINE, with studies dating back to the last decade. Of the 31 studies that were eventually included in the final review, there is no statistically clear superiority and real benefit from lymph node resection beyond the lymph nodes of the mid-rectum. European guidelines are set against lateral lymph node dissection, except for lymph nodes that show suspicious features on preoperative imaging. In contrast, in Eastern countries, total mesorectal excision (TME) with extensive simultaneous resection of the lateral pelvic lymph nodes (LPLNs) is the protocol followed. Recent studies focus on the subcategory of patients with non-responsive to adjuvant therapy, lateral lymph nodes, in which the ultimate benefit of extensive lymph node dissection is explored. The decision to join the TME procedure for the removal of the LPLNs is a subject of intense research. There are no data on the criteria for determining these lymph nodes as an increased risk of metastatic outbreaks. Despite the great clinical and research interest worldwide nowadays, the resection of LPLNs remains a controversial issue of debate, with intense disagreements according to geographical area, while the existence of additional studies is necessary to come to final conclusions.

Nodal staging with MRI after neoadjuvant chemo-radiotherapy for locally advanced rectal cancer: a fast and reliable method

OBJECTIVES

In patients with locally advanced rectal carcinoma (LARC), negative nodal status after neoadjuvant chemoradiotherapy (nCRT) may allow for rectum-sparing protocols rather than total mesorectal excision; however, current MRI criteria for nodal staging have suboptimal accuracy. The aim of this study was to compare the diagnostic accuracy of different MRI dimensional criteria for nodal staging after nCRT in patients with LARC.

MATERIALS AND METHODS

Patients who underwent MRI after nCRT for LARC followed by surgery were retrospectively included and divided into a training and a validation cohort of 100 and 39 patients, respectively. Short-, long-, and cranial-caudal axes and volume of the largest mesorectal node and nodal status based on European Society of Gastrointestinal Radiology consensus guidelines (i.e., ESGAR method) were assessed by two radiologists independently. Inter-reader agreement was assessed in the training cohort. Histopathology was the reference standard. ROC curves and the best cut-off were calculated, and accuracies compared with the McNemar test.

RESULTS

The study population included 139 patients (median age 62 years [IQR 55-72], 94 men). Inter-reader agreement was high for long axis (κ = 0.81), volume (κ = 0.85), and ESGAR method (κ = 0.88) and low for short axis (κ = 0.11). Accuracy was similar (p > 0.05) for long axis, volume, and ESGAR method both in the training (71%, 74%, and 65%, respectively) and in the validation (83%, 78%, and 75%, respectively) cohorts.

CONCLUSION

Accuracy of the measurement of long axis and volume of the largest lymph node is not inferior to the ESGAR method for nodal staging after nCRT in LARC.

CLINICAL RELEVANCE STATEMENT

In MRI restaging of rectal cancer, measurement of the long axis or volume of largest mesorectal lymph node after preoperative chemoradiotherapy is a faster and reliable alternative to ESGAR criteria for nodal staging.

KEY POINTS

• Current MRI criteria for nodal staging in locally advanced rectal cancer after chemo-radiotherapy have suboptimal accuracy and are time-consuming. • Measurement of long axis or volume of the largest mesorectal lymph node on MRI showed good accuracy for assessment of loco-regional nodal status in locally advanced rectal cancer. • MRI measurement of the long axis and volume of largest mesorectal lymph node after chemo-radiotherapy could be a faster and reliable alternative to ESGAR criteria for nodal staging.

Controversy in the initial nodal staging of rectal cancer (MRI or PET/CT?)

OBJECTIVE

To compare the usefulness of MRI and PET/CT in nodal staging (N) of patients with locally advanced rectal cancer (LARC).

MATERIAL AND METHODS

Retrospective study of patients with LARC, who completed their initial staging with PET/CT, between January-20 and March-23. Regional nodes were assessed, and N was determined using both techniques according to TNM criteria. Concordance between MRI and PET/CT was analyzed. The accuracy of both techniques was calculated for those patients who underwent direct surgery. Non-regional pelvic lymph nodes were evaluated by both modalities.

RESULTS

Among the 73 patients, 48 were ultimately diagnosed with a locally advanced stage. Of these, 39 underwent neoadjuvant treatment (chemoradiotherapy) followed by surgery, and 9 direct surgery. In 25, the PET/CT extension study revealed distant disease, leading to systemic treatment. Weak concordance was observed between MRI and PET/CT in determining N (k=0.286; p<0.005). Out of 73 patients, 31(42%) exhibited concordance, and 42(58%) showed discordance. In 83% of the discordant cases, MRI overstaged compared to PET/CT, with 17 cases indicating nodal involvement (N+) by MRI and N0 by PET/CT. Diagnostic accuracy was 78% for both techniques. Sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 75%, 80%, and 75% for MRI, and 60%, 100%, 100%, and 67%, for PET/CT. PET/CT identified pelvic metastatic adenopathies in 8 patients that were not visible/doubtful by MRI.

CONCLUSIONS

In the initial nodal staging of rectal cancer MRI overstages relative to PET/CT. Both modalities are complementary, PET/CT offers higher specificity and MRI higher sensitivity.

Magnetic resonance imaging for assessment of rectal cancer nodes after chemoradiotherapy: A single center experience

BACKGROUND

Accurate nodal restaging is becoming clinically more important in patients with locally advanced rectal cancer (LARC) with the emergence of organ-preserving treatment after a good response to neoadjuvant chemoradiotherapy (nCRT).

PURPOSE

To evaluate the accuracy of MRI in identifying negative N status (ypN0 patients) in LARC after nCRT.

MATERIAL AND METHODS

191 patients with LARC underwent MRI before and 6-8 weeks after nCRT and subsequent total mesorectal excision. Short-axis diameter of mesorectal lymph nodes was evaluated on the high resolution T2-weighted images to compare MRI restaging with histopathology..

RESULTS

146 and 45 patients had a negative N status (ypN0) and positive N status (ypN + ), respectively. On restaging MRI, the 70 % reduction in size of the largest node was associated with an area under the curve (AUC) of 0.818 to predict ypN0 stage, with a sensitivity of 93.3 % and a negative predictive value (NPV) of 95.4 %. No nodes were observed in 38 pts (37 pts ypN0 and 1 patient ypN + ), with sensitivity and NPV of nodes disappearance for ypN0 stage of 93.3 % and 92.5 % respectively. A 2.2 mm cut-off in short-axis diameter was associated with an AUC of 0.83 for the prediction of ypN0 nodal stage, with sensitivity and NPV of 79,5% and 91.1 % respectively.

CONCLUSION

A reduction in size of 70 % of the largest limph-node on MRI at rectal cancer restaging has high sensitivity and NPV for prediction of ypN0 stage after nCRT. The high NPV of node disappearance and of a ≤ 2.2 mm short-axis diameter is confirmed.

Evolution of therapy for locally advanced rectal cancer

Rectal cancer is a prevalent disease worldwide. The standard treatment of locally advanced rectal cancer (LARC) is preoperative chemoradiotherapy followed by surgery and adjuvant systemic chemotherapy. Studies have been done to determine the best sequence of treatments to improve survival, cure rate and long term toxicity profile. In this paper, we will review the literature regarding the evolution of LARC treatment.

Use of a radiomics-clinical model based on magnetic diffusion-weighted imaging for preoperative prediction of lymph node metastasis in rectal cancer patients

Rectal cancer is the eighth most prevalent malignancy worldwide with a 3.2% mortality rate and 3.9% incidence rate. Radiologists still have difficulty in correctly diagnosing lymph node metastases that have been suspected preoperatively. To assess the effectiveness of a model combining clinical and radiomics features for the preoperative prediction of lymph node metastasis in rectal cancer. We retrospectively analyzed data from 104 patients with rectal cancer. All patients were selected as samples for the training (n = 72) and validation cohorts (n = 32). Lymph nodes (LNs) in diffusion-weighted images were analyzed to obtain 842 radiomic characteristics, which were then used to draw the region of interest. Logistic regression, least absolute shrinkage and selection operator, and between-group and within-group correlation analyses were combined to establish the radiomic score (rad-score). Receiver operating characteristic curves were used to estimate the prediction accuracy of the model. A calibration curve was constructed to test the predictive ability of the model. A decision curve analysis was performed to analyze the model's value in clinical application. The area under the curve for the radiomics-clinical, clinical, and radiomics models was 0.856, 0.810, and 0.781, respectively, in the training cohort and 0.880, 0.849, and 0.827, respectively, in the validation cohort. The calibration curve and DCA showed that the radiomics-clinical prediction model had good prediction accuracy, which was higher than that of the other models. The radiomics-clinical model showed a favorable predictive performance for the preoperative prediction of LN metastasis in patients with rectal cancer.

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.

Nonoperative Management of dMMR/MSI-H Colorectal Cancer following Neoadjuvant Immunotherapy: A Narrative Review

Immunotherapy with PD-1 blockade has achieved a great success in colorectal cancers (CRCs) with high microsatellite instability (MSI-H) and deficient mismatch repair (dMMR), and has become the first-line therapy in metastatic setting. Studies of neoadjuvant immunotherapy also report exciting results, showing high rates of clinical complete response (cCR) and pathological complete response. The high efficacy and long duration of response of immunotherapy has prompt attempts to adopt watch-and-wait strategy for patients achieving cCR following the treatment. Thankfully, the watch-and-wait approach has been proposed for nearly 20 years for patients undergoing chemoradiotherapy and has gained ground among patients as well as clinicians. In this narrative review, we combed through the available information on immunotherapy for CRC and on the watch-and-wait strategy in chemoradiotherapy, and looked forward to a future where neoadjuvant immunotherapy as a curative therapy would play a big part in the treatment of MSI-H/dMMR CRC.

MRI Evaluation of Rectal Cancer Lymph Node Staging Using Apparent Diffusion Coefficient

Introduction Colorectal cancer is the third most diagnosed cancer globally. Lymph node metastases significantly affect prognosis, emphasizing the importance of early detection and management. Despite significant advances in conventional MRI's role in staging, improvements in advanced functional imaging such as diffusion-weighted imaging (DWI) in identifying lymph node metastases persist. Objectives The aim is to evaluate the effectiveness of apparent diffusion coefficient (ADC) MRI in evaluating lymph node staging in rectal cancer. Patients and methods In a prospective study, 89 patients with stage II-III rectal cancer were grouped into two treatments: pre-operative FOLFOX4 chemotherapy and standard pre-operative chemoradiotherapy. All underwent 1.5T MRI, with T2-weighted and DWI sequences. A radiologist defined regions of interest on the tumor, lymph nodes, and intact rectal wall to calculate ADC values. Results Rectal cancer ADC's receiver operating characteristic curve had an area under the curve (AUC) of 0.688 (P < 0.001), with optimal ADC cutoff at 0.99 x 10-3 mm2/s (sensitivity: 75%, specificity: 83%). For lymph nodes, AUC was 0.508 (P < 0.001), with a cutoff of 0.9 x 10-3 mm2/s (sensitivity: 78%, specificity: 67%). No correlation between tumor and lymph node ADC values was observed. In chemotherapy patients, "healthy" inguinal lymph nodes had higher ADC values than affected ones pre-treatment (P = 0.001), a disparity fading post-treatment (P = 0.313). For chemoradiotherapy patients, the ADC difference persisted pre and post-treatment (P = 0.001). Conclusion The study of ADC-MRI showed different ADC values between tumors and lymph nodes and highlighted ADC differences between treatment groups. Notably, no correlation was observed between tumor and lymph node ADC values. However, differences were apparent when comparing "healthy" inguinal nodes with lymph nodes affected by cancer.

Vascular Enlargement as a Predictor of Nodal Involvement in Bladder Cancer

In bladder cancer (BC), the evaluation of lymph node (LN) involvement at preoperative imaging lacks specificity. Since neoangiogenesis is paired with lymphatic involvement, this study aims to evaluate the presence of perivesical venous ectasia as an indirect sign of LN involvement, together with other conventional CT findings. All the patients who underwent radical cystectomy (RC) for BC between January 2017 and December 2019 with available preoperative contrast-enhanced CT (CECT) within 1 month before surgery were included. Patients without available pathological reports (and pTNM stage) or who underwent neoadjuvant treatments and palliative RC were excluded. Two readers in blind assessed the nodal shape and hilum, the short axis, and the contrast enhancement of suspicious pelvic LNs, the Largest Venous Diameter (LVD) efferent to the lesion, and the extravesical tumor invasion. In total, 38 patients (33 males) were included: 17 pT2, 17 pT3, 4 pT4; pN+: 20/38. LN short axis > 5 mm, LN enhancement, and LVD > 3 mm were significantly correlated with N+ at pathology. LVD > 3 mm had a significantly higher sensitivity and specificity (≥90%, AUC = 0.949) and was an independent predictor (p = 0.0016).

Rectal Cancer: Are 12 Lymph Nodes the Limit?

Lymph node dissection is a crucial element of oncologic rectal surgery. Many guidelines regard the removal of at least 12 lymph nodes as the quality criterion in rectal cancer. However, this recommendation remains controversial. This study examines the factors influencing the lymph node yield and the validity of the 12-lymph node limit. Patients with rectal cancer who underwent low anterior resection or abdominoperineal amputation between 2000 and 2010 were analyzed. In total, 20,966 patients from 381 hospitals were included. Less than 12 lymph nodes were found in 20.53% of men and 19.31% of women (p = 0.03). The number of lymph nodes yielded increased significantly from 2000, 2005 and 2010 within the quality assurance program for all procedures. The univariate analysis indicated a significant (p < 0.001) correlation between lymph node yield and gender, age, pre-therapeutic T-stage, risk factors and neoadjuvant therapy. The multivariate analyses found T3 stage, female sex, the presence of at least one risk factor and neoadjuvant therapy to have a significant influence on yield. The probability of finding a positive lymph node was proportional to the number of examined nodes with no plateau. There is a proportional relationship between the number of examined lymph nodes and the probability of finding an infiltrated node. Optimal surgical technique and pathological evaluation of the specimen cannot be replaced by a numeric cut-off value.

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.

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.

Role of apparent diffusion coefficient in assessment of loco-regional nodal spread in cancer rectum: correlative study with histopathological findings

Background

Rectal cancer is associated with high morbidity and mortality rates. Preoperative assessment and detection of nodal metastasis are crucial for selecting a proper treatment plan. Diffusion-weighted imaging is considered to be a crucial functional imaging technique that can aid in determining the condition of lymph nodes. This study aimed to assess the diagnostic utility of MRI functional images by use of apparent diffusion coefficient in regional nodal assessment in rectal cancer.

Results

This study included 54 patients including 29 males (53.7%) and 25 females (46.3%) presented with pathologically proven rectal cancer. Regarding rectal adenocarcinoma, functional MRI imaging using ADC values found to have a better sensitivity (86.24%) in detection of regional nodal metastasis than conventional morphological MRI criteria with 1.05 × 10−3 mm2/s was employed as cutoff value to distinguish metastatic from non-metastatic lymph nodes with statistically significant P value (< 0.001); nevertheless, regarding the accuracy there was no difference (68.52%). As regards mucinous and signet ring cell carcinoma, morphological assessment using conventional MRI sequences were found to have a better accuracy (72.96%) and sensitivity (57.69%) than ADC value, with the latter showed low statistically significant results (P- value < 0.201) in distinguishing metastatic and non-metastatic nodes. This could be explained by extremely high ADC values of nodes for these pathological types owing to their high mucin content.

Conclusions

MRI functional imaging using ADC values can be utilized to distinguish metastatic from non-metastatic lymph nodes in rectal adenocarcinoma employing diagnostic accuracy of 86.52%. However, morphological assessment using conventional MRI was found to be better in assessment of regional lymph nodes at mucinous and signet ring rectal carcinoma.

Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications

Rectal cancer (RC) is one of the deadliest malignancies worldwide. Surgery is the most common treatment for RC, performed in 63.2% of patients. The type of surgical approach chosen aims to achieve maximum residual function with the lowest risk of recurrence. The selection is made by a multidisciplinary team that assesses the characteristics of the patient and the tumor. Total mesorectal excision (TME), including both low anterior resection (LAR) and abdominoperineal resection (APR), is still the standard of care for RC. Radical surgery is burdened by a 31% rate of major complications (Clavien-Dindo grade 3-4), such as anastomotic leaks and a risk of a permanent stoma. In recent years, less-invasive techniques, such as local excision, have been tested. These additional procedures could mitigate the morbidity of rectal resection, while providing acceptable oncologic results. The "watch and wait" approach is not a globally accepted model of care but encouraging results on selected groups of patients make it a promising strategy. In this plethora of treatments, the radiologist is called upon to distinguish a physiological from a pathological postoperative finding. The aim of this narrative review is to identify the main post-surgical complications and the most effective imaging techniques.

Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.

Immunotherapy Assessment: A New Paradigm for Radiologists

Immunotherapy denotes an exemplar change in an oncological setting. Despite the effective application of these treatments across a broad range of tumors, only a minority of patients have beneficial effects. The efficacy of immunotherapy is affected by several factors, including human immunity, which is strongly correlated to genetic features, such as intra-tumor heterogeneity. Classic imaging assessment, based on computed tomography (CT) or magnetic resonance imaging (MRI), which is useful for conventional treatments, has a limited role in immunotherapy. The reason is due to different patterns of response and/or progression during this kind of treatment which differs from those seen during other treatments, such as the possibility to assess the wide spectrum of immunotherapy-correlated toxic effects (ir-AEs) as soon as possible. In addition, considering the unusual response patterns, the limits of conventional response criteria and the necessity of using related immune-response criteria are clear. Radiomics analysis is a recent field of great interest in a radiological setting and recently it has grown the idea that we could identify patients who will be fit for this treatment or who will develop ir-AEs.

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.