Multiparametric MRI with pea pod sign and muscularis propria striped enhancement improves T2 vs T3a-b rectal cancer staging accuracy.

BackgroundThis study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer. MethodsWe included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data.Results After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the trainingset and 0.920 in the test set.ConclusionThe study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.

To develop and validate a nomogram for the preoperative diagnosis of T2 and T3 stage rectal cancer using MRI radiomics features of mesorectal fat. The data of 288 patients with T2 and T3 stage rectal cancer were retrospectively collected. Radiomics features were extracted from the lesion region of interest (ROI) in the MRI high-resolution T2WI, apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences. After using ICC inter-group consistency analysis and Pearson correlation analysis to reduce dimensions, LASSO regression analysis was performed to select features and calculate Rad-score for each sequence. Then, Combined_Radscore and nomogram were constructed based on the LASSO-selected features and clinical data for each sequence. Receiver operating characteristic curve (ROC) area under the curve (AUC) was used to evaluate the performance of the Rad-score model and nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical usability of the radiomics nomogram, which were combined with calibration curves to evaluate the prediction accuracy. The nomogram based on MRI-report T status and Combined_Radscore achieved AUCs of 0.921 and 0.889 in the training and validation cohorts, respectively. The nomogram can be stated that the radiomics nomogram based on multi-sequence MRI imaging of the mesorectal fat has excellent diagnosing performance for preoperative differentiation of T2 and T3 stage rectal cancer.

BackgroundEarly rectal neoplasms can be treated endoscopically with good prognosis, yet usually present with unspecific or an absence of signs and symptoms and are detected largely by invasive endoscopy with less compliance to screening. The purpose of this cross-sectional study was to explore the diagnostic value of dual-layer spectral detector computed tomography (DSCT) imaging for early rectal neoplasm.MethodsPatients who underwent DSCT for evaluation of rectal lesion or routine examination between September 2022 to September 2023 at West China Hospital were prospectively included and identified as group A (control, n=76), group B (rectal advanced adenomas and ≤T1 rectal cancer, n=59), and group C (≥T2 staging rectal cancer, n=74). Lesion visualization was graded to assess image quality. Spectral quantitative measurement, such as Hounsfield unit (HU)40 keV, HU70 keV, iodine concentration (IC), effective atomic number (Zeff), and the slope of spectral curve (λ), was analyzed and compared. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic efficacy of spectral parameters. A comparison of ROC curves was applied to test the significance of differences between the area under the curves (AUCs).ResultsCompared to poly-energetic images (PEIs), the multiple parameters from DSCT were of greater capability to recognize rectal lesions. There were significant differences in HU40 keV (208.01±43.60 vs. 255.53±45.16), HU70 keV (87.06±18.55 vs. 100.78±18.26), IC [1.91 (1.71, 2.28) vs. 2.58±0.49], Zeff [8.33 (8.25, 8.50) vs. 8.61±0.20], and λ [3.80 (3.41, 4.52) vs. 5.16±1.00] between the early neoplastic lesions in rectum and the advanced rectal cancer (P<0.001). Significant correlations were found between the DSCT parameters and tumor staging (P<0.001). Furthermore, the AUCs of IC, Zeff, λ, and HUPEI were all above 0.90 for early rectal neoplasm detection, with additional capability of discriminating early rectal neoplasm from advanced rectal cancer.ConclusionsDSCT improved tumor conspicuity and the detection of the early rectal neoplastic lesion, suggesting that it is a promising screening tool in clinical practice.

EUS staging of primary lung carcinoma: are we ready for it?

With the implementation of screening programs worldwide, diagnosis of early-stage colorectal cancer steadily increased, including T1 cancer. Current T1 cancer treatment does not differ according to anatomic location. We therefore compared the disease-free survival of T1 cancer arising from the rectum versus the colon. The hospital-based study included subjects with T1 cancer at National Taiwan University Hospital from 2005 to 2014. Clinical, colonoscopy, and histopathology were reviewed for patients with a mean follow-up time of 7.1 (0.7-12.9) years. We conducted Kaplan-Meier analysis to compare the risk of recurrence by cancer location and Cox regression analysis to identify risk factors for T1 cancer recurrence. The final cohort included a total of 343 subjects with T1 cancer (mean age, 64.9± 11.7 years; 56.1% male), of whom 25 underwent endoscopic resection alone. Of the subjects who underwent surgery, 50 had lymph node metastasis and 268 did not. Kaplan-Meier analysis showed that the risk of recurrence was higher in T1 rectal cancer than T1 colon cancer (p=.022). Rectal location and larger neoplasm size were independent risk factors for recurrence, with hazard ratios of 4.84 (95% confidence interval, 1.18-19.92), and 1.32 (95% confidence interval, 1.06-1.65), respectively. The occurrence of advanced histology did not differ between T1 rectal and colon cancers (p=.58). T1 cancers arising from the rectum had less favorable recurrence outcomes than those arising from the colon. Further studies are needed to examine whether adjuvant radiotherapy or chemotherapy can reduce the risk of recurrence in T1 rectal cancer. Current T1 colorectal cancer treatment and surveillance do not differ according to anatomic location. Clinical, colonoscopy, and histopathology were reviewed for 343 patients with T1 cancer with a mean follow-up time of 7.1 years. Kaplan-Meier analysis showed that the risk of recurrence was higher in T1 rectal cancer than T1 colon cancer. Moreover, the rectal location was an independent risk factor for recurrence. T1 cancers from the rectum had less favorable recurrence outcomes than those arising from the colon. It is critical to clarify whether adjuvant therapy or more close surveillance can reduce recurrence risk in T1 rectal cancer.

Submucosal Enhancing Stripe: An Important Contrast-enhanced MRI Feature for Staging of Rectal Cancers.

A High-resolution T2WI-based Deep Learning Model for Preoperative Discrimination Between T2 and T3 Rectal Cancer: A Multicenter Study.

Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.

Transrectal Ultrasound (TRUS) Helps Select Patients for Transanal Excision (TAE) of Early (T1) Rectal Cancers and Large Polyps

Automatic Prediction of T2/T3 Staging of Rectal Cancer Based on Radiomics and Machine Learning

Preoperative chemoradiotherapy is known to reduce the local recurrence of locally advanced rectal cancer. However, the careful use of preoperative chemoradiotherapy is essential, because unnecessary over‐treatment can result in unintended complications. Therefore, a diagnostic system for distinguishing between T2 and T3 rectal cancers should be developed. According to the diagnostic criteria for rectal cancer, radiologists first identify the locations and the shapes of both the tumor and the rectum from a medical image and then diagnose the T2/T3 rectal cancer by determining whether the tumor passes through the rectal wall or not. We construct two distinct convolutional neural network models to achieve the automated segmentation of each rectum and tumor, respectively. Then, we construct another convolutional neural network model, which uses the output images of segmentation models as input and determines whether the tumor in the input magnetic resonance image is at the T2 stage or at the T3 stage. We evaluate the effectiveness of the proposed method based on 290 magnetic resonance images from 133 subjects. The proposed model demonstrates an accuracy of 94%.

ObjectiveThe present research aimed to evaluate the diagnostic performance of a magnetic resonance imaging (MRI)-based radiomics model for predicting lymph node staging in patients with stage T3 rectal cancer (RC).MethodsThis retrospective study included 225 patients with RC who underwent surgical resection without neoadjuvant therapy treatment. Radiomics features were extracted from high-resolution T2-weighted imaging (T2WI) of primary tumor. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning classifiers were employed to construct radiomics signatures differentiating between N0/N1 (low nodal burden) and N2 (high nodal burden) stages prediction in the training cohort. The predictive performance of each classifier was evaluated using receiver operating characteristic curve analysis, with area under the curve (AUC) comparisons conducted via DeLong’s test. Decision curve analysis (DCA) and calibration curves were utilized to assess the clinical utility and calibration performance of the developed models, respectively.ResultsA total of 1,746 radiomics features were extracted from the imaging data, of which 16 features were selected to construct a radiomics signature for lymph node staging in RC. The logistic regression classifier demonstrated the best predictive performance, achieving an AUC of 0.900 [95% confidence interval (CI), 0.848–0.952] in the training cohort. The model’s robustness was further validated in the test cohort, with an AUC of 0.876 (95% CI, 0.765–0.986). DCA confirmed the clinical utility of the model.ConclusionsThe radiomics model based on high-resolution T2WI provided an effective and noninvasive approach for preoperatively differentiating between N0/1 and N2 stages in stage T3 RC.

The Safety of Endosonography-Guided FNA and/or Trucut Biopsy in Patients on Aspirin, NSAIDs or Prophylactic Low Molecular Weight Heparin

To establish and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI), and to predict microsatellite instability (MSI) status in rectal cancer patients. A total of 199 patients with pathologically confirmed rectal cancer were included. The MSI status was confirmed by immunohistochemistry (IHC) staining. Clinical factors and laboratory data associated with MSI status were analyzed. The imaging data of 100 patients from one of the hospitals were used as the training set. The remaining 99 patients from the other two hospitals were used as the external validation set. The regions of interest (ROIs) were delineated from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) sequence to extract the radiomics features. The Tree-based approach was used for feature selection. The models were constructed based on the four single sequences and a combination of the four sequences using the random forest (RF) algorithm. The external validation set was used to verify the generalization ability of each model. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each model. In the four single-series models, the CE-T1WI model performed the best. The AUCs of the T1WI, T2WI, DWI, and CE-T1WI prediction models in the training set were 0.74, 0.71, 0.71, and 0.78, respectively, while in the external validation set, the corresponding AUCs were 0.67, 0.66, 0.70, and 0.77. The prediction and generalization performance of the combined model of multi-sequences was comparable to that of the CE-T1WI model and it was better than that of the remaining three single-series models, with AUC values of 0.78 and 0.78 in the training and validation sets, respectively. The established radiomics models based on CE-T1WI or multiparametric MRI have similar predictive performance. They have the potential to predict MSI status in rectal cancer patients. • A radiomics model for the prediction of MSI status in patients with rectal cancer was established and validated using external validation. • The models based on CE-T1WI or multiparametric MRI have better predictive performance than those based on single unenhanced sequence images. • The radiomics model has the potential to suggest MSI status in rectal cancer patients; however, it is not yet a substitute for histological confirmation.

Chemo-radiotherapy (CRT) after local excision for pT1 with high-risk features or pT2 rectal cancer is recommended as an optional treatment to achieve both curability and maintenance of quality of life. The aim of this study was to evaluate the short-term safety of combining limited surgery with adjuvant CRT for T1 or T2 lower rectal cancer. This was a multicenter, single-arm, prospective phase II trial. Patients diagnosed with lower rectal or anal canal cancer (clinical T1 or T2 with a maximum diameter of 30mm and N0 and M0) underwent local excision or endoscopic resection. Patients received CRT with S-1 (tegafur/gimeracil/oteracil) after confirmation of well- or moderately differentiated adenocarcinoma, and negative margins, and/or depth of submucosal invasion ≥ 1000µm or muscularis propria, and/or positive lymphovascular invasion, and/or tumor budding grade of 2/3. The primary endpoint was relapse-free survival. Secondary endpoints included overall and local relapse-free survival, safety, anal sphincter preservation rate, and anal function. Pathological diagnosis was T1 in 36 patients and T2 in 16 patients. Serious complications after surgery were not reported. The CRT completion rate per protocol was 86.5% (45/52). Thirty-two patients developed 54 events of CRT-related adverse events, including only one patient with a grade 3 event (stomatitis). The most common CRT-related adverse event was diarrhea (n = 14). No patients showed deterioration of anal function at 3years postoperatively. CRT with S-1 after limited surgery for T1 or T2 lower rectal cancer resulted in a low incidence of toxicities and maintenance of anal function.