Frans C. H. Bakers

Long-term follow-up features on rectal MRI during a wait-and-see approach after a clinical complete response in patients with rectal cancer treated with chemoradiotherapy.

BACKGROUND: The “wait-and-see” policy instead of standard surgery for patients with rectal cancer who undergo a complete tumor regression after chemoradiation treatment is highly controversial. It is not clear yet how patients should be monitored once they are managed nonoperatively and whether follow-up by MRI has any potential role. OBJECTIVE: This study aimed to describe the rectal wall MRI morphology during short-term and long-term follow-up in patients with a clinical complete tumor response undergoing a wait-and-see policy without surgical treatment. DESIGN, SETTING, AND PATIENTS: As part of an observational study in our center, a cohort of 19 carefully selected patients with a clinical complete response after chemoradiation was managed with a wait-and-see policy and followed regularly (every 3–6 mo) by clinical examination, endoscopy with biopsies, and a rectal MRI. The MR morphology of the tumor bed was studied on the consecutive MRI examinations. MAIN OUTCOME MEASURES: The primary outcome measured was the morphology of the tumor bed on the consecutive MRI examinations performed during short-term (≤6 mo) and long-term (>6 mo) follow-up. RESULTS: Patients with a complete tumor response after chemoradiation presented with either a normalized rectal wall (26%) or fibrosis (74%). In the latter group, 3 patterns of fibrosis were observed (full-thickness, minimal, or spicular fibrosis). The morphology patterns of a normalized rectal wall or fibrosis remained consistent during long-term follow-up in 18 of 19 patients. One patient developed a small, endoluminal recurrence, which was salvaged with transanal endoscopic microsurgery. In 26% of patients, an edematous wall thickening was observed in the first months after chemoradiation, which gradually decreased during long-term follow-up. Median follow-up was 22 months (range, 12–60). LIMITATIONS: This was a small observational study, and had no histological validation. CONCLUSIONS: Four MR patterns of a persistent complete response of rectal cancer after chemoradiation were identified. These MR features can serve as a reference for the follow-up in a wait-and-see policy.

FDG-PET provides the best correlation with the tumor specimen compared to MRI and CT in rectal cancer

PURPOSE To compare CT-, MR- and PET-CT based tumor length measurements in rectal cancer with pathology. PATIENTS AND METHODS Twenty-six rectal cancer patients underwent both MR and PET-CT imaging followed by short-course radiotherapy (RT 5×5 Gy) and surgery within 3 days after RT. Tumor length was measured manually and independently by 2 observers on CT, MR and PET. PET-based tumor length measurements were also generated automatically using the signal-to-background-ratio (SBR) method. All measurements were correlated with the tumor length on the pathological specimen. RESULTS CT-based measurements did not show a valuable correlation with pathology. MR-based measurements correlated only weakly, but still significantly (Pearson correlation=0.55 resp. 0.57; p<0.001). Manual PET measurements reached a good correlation with pathology, but less strong (Pearson correlation 0.72 and 0.76 for the two different observers) than automatic PET-CT based measurements, which provided the best correlation with pathology (Pearson correlation of 0.91 (p<0.001)). Bland-Altman analysis demonstrated in general an overestimation of the tumor diameter using manual measurements, while the agreement of automatic contours and pathology was within acceptable ranges. A direct comparison of the different modalities revealed a significant better precision for PET-based auto-contours as compared to all other measurements. CONCLUSION Automatically generated PET-CT based contours show the best correlation with the surgical specimen and thus provide a useful and powerful tool to accurately determine the largest tumor dimension in rectal cancer. This could be used as a quick and reliable tool for target delineation in radiotherapy. However, a 3D volume analysis is needed to confirm these results.

Morphological MRI criteria improve the detection of lymph node metastases in head and neck squamous cell carcinoma: multivariate logistic regression analysis of MRI features of cervical lymph nodes

The aim was to evaluate whether morphological criteria in addition to the size criterion results in better diagnostic performance of MRI for the detection of cervical lymph node metastases in patients with head and neck squamous cell carcinoma (HNSCC). Two radiologists evaluated 44 consecutive patients in which lymph node characteristics were assessed with histopathological correlation as gold standard. Assessed criteria were the short axial diameter and morphological criteria such as border irregularity and homogeneity of signal intensity on T2-weighted and contrast-enhanced T1-weighted images. Multivariate logistic regression analysis was performed: diagnostic odds ratios (DOR) with 95% confidence intervals (95% CI) and areas under the curve (AUCs) of receiver-operating characteristic (ROC) curves were determined. Border irregularity and heterogeneity of signal intensity on T2-weighted images showed significantly increased DORs. AUCs increased from 0.67 (95% CI: 0.61–0.73) using size only to 0.81 (95% CI: 0.75–0.87) using all four criteria for observer 1 and from 0.68 (95% CI: 0.62–0.74) to 0.96 (95% CI: 0.94–0.98) for observer 2 (p < 0.001). This study demonstrated that the morphological criteria border irregularity and heterogeneity of signal intensity on T2-weighted images in addition to size significantly improved the detection of cervical lymph nodes metastases.

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations suggest that volumetric analyses on anatomical and functional MRI contain clinically valuable information. However, manual delineation of tumours is a time consuming procedure, as it requires a high level of expertise. Here, we evaluate deep learning methods for automatic localization and segmentation of rectal cancers on multiparametric MR imaging. MRI scans (1.5T, T2-weighted, and DWI) of 140 patients with locally advanced rectal cancer were included in our analysis, equally divided between discovery and validation datasets. Two expert radiologists segmented each tumor. A convolutional neural network (CNN) was trained on the multiparametric MRIs of the discovery set to classify each voxel into tumour or non-tumour. On the independent validation dataset, the CNN showed high segmentation accuracy for reader1 (Dice Similarity Coefficient (DSC = 0.68) and reader2 (DSC = 0.70). The area under the curve (AUC) of the resulting probability maps was very high for both readers, AUC = 0.99 (SD = 0.05). Our results demonstrate that deep learning can perform accurate localization and segmentation of rectal cancer in MR imaging in the majority of patients. Deep learning technologies have the potential to improve the speed and accuracy of MRI-based rectum segmentations.

Diagnostic Accuracy of CT for Local Staging of Colon Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE The purpose of this article is to determine the accuracy of CT in the detection of tumor invasion beyond the bowel wall and nodal involvement of colon carcinomas. A literature search was performed to identify studies describing the accuracy of CT in the staging of colon carcinomas. Studies including rectal carcinomas that were inseparable from colon carcinomas were excluded. Publication bias was explored by using a Deeks funnel plot asymmetry test. A hierarchic summary ROC model was used to construct a summary ROC curve and to calculate summary estimates of sensitivity, specificity, and diagnostic odds ratios (ORs). CONCLUSION On the basis of a total of 13 studies, pooled sensitivity, specificity, and diagnostic ORs for detection of tumor invasion beyond the bowel wall (T3-T4) were 90% (95% CI, 83-95%), 69% (95% CI, 62-75%), and 20.6 (95% CI, 10.2-41.5), respectively. For detection of tumor invasion depth of 5 mm or greater (T3cd-T4), estimates from four studies were 77% (95% CI, 66-85%), 70% (95% CI, 53-83%), and 7.8 (95% CI, 4.2-14.2), respectively. For nodal involvement (N+), 16 studies were included with values of 71% (95% CI, 59-81%), 67% (95% CI, 46-83%), and 4.8 (95% CI, 2.5-9.4), respectively. Two studies using CT colonography were included with sensitivity and specificity of 97% (95% CI, 90-99%) and 81% (95% CI, 65-91%), respectively, for detecting T3-T4 tumors. CT has good sensitivity for the detection of T3-T4 tumors, and evidence suggests that CT colonography increases its accuracy. Discriminating between T1-T3ab and T3cd-T4 cancer is challenging, but data were limited. CT has a low accuracy in detecting nodal involvement.

Diagnostic accuracy of preoperative tests for lymph node status in endometrial cancer: a systematic review.

Abstract Background: Approximately 72% of endometrial cancers are FIGO stage I at diagnosis and about 10% have lymph node metastases. An ideal diagnostic test for nodal disease would be able to prevent both overtreatment (i.e. unnecessary lymphadenectomy) and undertreatment (i.e. withholding lymphadenectomy or adjuvant postoperative treatment to patients with lymph node metastases). Objectives: In this review we compare the accuracy of preoperative tests (computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography, CA-125 serum levels, and ultrasonography) for the detection of lymph node metastases in endometrial cancers with the final histopathologic diagnosis after complete pelvic and para-aortic lymphadenectomy as the gold standard. Method: A systematic search in MEDLINE (using PubMed), Embase and The Cochrane Library was performed up to 23 July 2012. Results: We found one article that met our inclusion criteria for computed tomography, none for magnetic resonance imaging, 2 for positron emission tomography/computed tomography), 2 for CA-125 and none for ultrasonography. Conclusions: Due to the lack of high-quality articles on a preoperative test for lymph node status in endometrial cancer, no proper comparison between these modalities can be made.

MRI for Local Staging of Colon Cancer : Can MRI Become the Optimal Staging Modality for Patients With Colon Cancer?

BACKGROUND: Colon cancer is currently staged with CT. However, MRI is superior in the detection of colorectal liver metastasis, and MRI is standard in local staging of rectal cancer. Optimal (local) staging of colon cancer could become crucial in selecting patients for neoadjuvant treatment in the near future (Fluoropyrimidine Oxaliplatin and Targeted Receptor Preoperative Therapy trial). OBJECTIVE: The purpose of this study was to evaluate the diagnostic performance of MRI for local staging of colon cancer. DESIGN: This was a retrospective study. SETTINGS: The study was conducted at the Maastricht University Medical Centre. PATIENTS: In total, 55 patients with biopsy-proven colon carcinoma were included. MAIN OUTCOME MEASURES: All of the patients underwent an MRI (1.5-tesla; T2 and diffusion-weighted imaging) of the abdomen and were retrospectively analyzed by 2 blinded, independent readers. Histopathology after resection was the reference standard. Both readers evaluated tumor characteristics, including invasion through bowel wall (T3/T4 tumors), invasion beyond bowel wall of ≥5 mm and/or invasion of surrounding organs (T3cd/T4), serosal involvement, extramural vascular invasion, and malignant lymph nodes (N+). Interobserver agreement was compared using &kgr; statistics. RESULTS: MRI had a high sensitivity (72%–91%) and specificity (84%–89%) in detecting T3/T4 tumors (35/55) and a low sensitivity (43%–67%) and high specificity (75%–88%) in detecting T3cd/T4 tumors (15/55). For detecting serosal involvement and extramural vascular invasion, MRI had a high sensitivity and moderate specificity, as well as a moderate sensitivity and specificity in the detection of nodal involvement. Interobserver agreements were predominantly good; the more experienced reader achieved better results in the majority of these categories. LIMITATIONS: The study was limited by its retrospective nature and moderate number of inclusions. CONCLUSIONS: MRI has a good sensitivity for tumor invasion through the bowel wall, extramural vascular invasion, and serosal involvement. In addition, together with its superior liver imaging, MRI might become the optimal staging modality for colon cancer. However, more research is needed to confirm this. See Video Abstract at http://links.lww.com/DCR/A309.

Neoplastic transformation of endocervicosis into an extraovarian mucinous cystadenocarcinoma

Although extraovarian mucinous cystadenocarcinomas resemble primary ovarian carcinomas, both histologically and clinically, their specific etiology is not clear. This is the first report to show neoplastic transformation of endocervicosis into an extraovarian mucinous cystadenocarcinoma. The histologic spectrum and specific KRAS mutational analysis for this tumor were the same as for their ovarian counterparts. This supports a müllerian origin and the current approach to extrapolate the results from ovarian mucinous cystadenocarcinoma trials in prescribing treatment for patients with extraovarian mucinous cystadenocarcinomas.

Diffusion-weighted MRI to assess response to chemoradiotherapy in rectal cancer: main interpretation pitfalls and their use for teaching

AbstractObjectivesTo establish the most common image interpretation pitfalls for non-expert readers using diffusion-weighted imaging (DWI) to assess response to chemoradiotherapy in patients with rectal cancer and to explore the use of these pitfalls in an expert teaching setting.MethodsTwo independent non-expert readers (R1 and R2) scored the restaging DW MRI scans (b1,000 DWI, in conjunction with ADC maps and T2-W MRI scans for anatomical reference) in 100 patients for the likelihood of a complete response versus residual tumour using a five-point confidence score. The readers received expert feedback and the final response outcome for each case. The supervising expert documented any potential interpretation errors/pitfalls discussed for each case to identify the most common pitfalls.ResultsThe most common pitfalls were the interpretation of low signal on the ADC map, small susceptibility artefacts, T2 shine-through effects, suboptimal sequence angulation and collapsed rectal wall. Diagnostic performance (area under the ROC curve) was 0.78 (R1) and 0.77 (R2) in the first 50 patients and 0.85 (R1) and 0.85 (R2) in the final 50 patients.ConclusionsFive main image interpretation pitfalls were identified and used for teaching and feedback. Both readers achieved a good diagnostic performance with an AUC of 0.85.Key Points• Fibrosis appears hypointense on an ADC map and should not be mistaken for tumour. • Susceptibility artefacts on rectal DWI are an important potential pitfall. • T2 shine-through on rectal DWI is an important potential pitfall. • These pitfalls are useful to teach non-experts how to interpret rectal DWI.

Prediction of incomplete primary debulking surgery in patients with advanced ovarian cancer: An external validation study of three models using computed tomography.

OBJECTIVE To test the ability of three prospectively developed computed tomography (CT) models to predict incomplete primary debulking surgery in patients with advanced (International Federation of Gynecology and Obstetrics stages III-IV) ovarian cancer. METHODS Three prediction models to predict incomplete surgery (any tumor residual >1cm in diameter) previously published by Ferrandina (models A and B) and by Gerestein were applied to a validation cohort consisting of 151 patients with advanced epithelial ovarian cancer. All patients were treated with primary debulking surgery in the Eastern part of the Netherlands between 2000 and 2009 and data were retrospectively collected. Three individual readers evaluated the radiographic parameters and gave a subjective assessment. Using the predicted probabilities from the models, the area under the curve (AUC) was calculated which represents the discriminative ability of the model. RESULTS The AUC of the Ferrandina models was 0.56, 0.59 and 0.59 in model A, and 0.55, 0.60 and 0.59 in model B for readers 1, 2 and 3, respectively. The AUC of Geresteins model was 0.69, 0.61 and 0.69 for readers 1, 2 and 3, respectively. AUC values of 0.69 and 0.63 for reader 1 and 3 were found for subjective assessment. CONCLUSIONS Models to predict incomplete surgery in advanced ovarian cancer have limited predictive ability and their reproducibility is questionable. Subjective assessment seems as successful as applying predictive models. Present prediction models are not reliable enough to be used in clinical decision-making and should be interpreted with caution.