Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

Clinical utility of sarcopenia dynamics assessed by psoas muscle volume in patients with colorectal cancer.

BACKGROUND: Sarcopenia affects the postoperative prognosis of patients with colorectal cancer (CRC). Recently, it has become possible to measure psoas volume from computed tomography images, and an index called psoas volume index (PVI) has been reported. However, it is unclear whether the dynamics of PVI before and after surgery is associated with clinical outcomes after CRC surgery. This study aimed to evaluate the association between pre- and postoperative PVI dynamics and clinical outcomes after CRC surgery. METHODS: This study analyzed 1115 patients diagnosed with primary CRC and operated on for treatment between January 2014 and December 2017. Sarcopenia was defined as PVI below the lowest tertile in the preoperative assessment for each sex. The overall population was divided into four groups according to the dynamics of sarcopenia from preoperative to postoperative: group 1 (pre-to postoperative sarcopenia), group 2 (preoperative nonsarcopenia to postoperative sarcopenia), group 3 (pre-to postoperative nonsarcopenia), and group 4 (pre-to postoperative nonsarcopenia). RESULTS: Based on pre- and postoperative sarcopenia dynamics, 343 patients (29.7%) were classified into group 1, 105 patients (9.1%) into group 2, 42 patients (3.6%) into group 3, and 665 patients (57.6%) into group 4. Comparison of overall survival (OS) by the Kaplan-Meier method showed that Group 2 tended to have the worst prognosis (p = 0.007). Multivariate analysis showed an increased OS risk in Group 2 in sarcopenia dynamics (Hazard ratio: 2.103, 95% CI: 1.202-3.681, p = 0.009). CONCLUSIONS: Sarcopenia dynamics using PVI is an independent prognostic predictor of OS in patients with CRC.

Can physician judgment enhance model trustworthiness? A case study on predicting pathological lymph nodes in rectal cancer.

Explainability is key to enhancing the trustworthiness of artificial intelligence in medicine. However, there exists a significant gap between physicians' expectations for model explainability and the actual behavior of these models. This gap arises from the absence of a consensus on a physician-centered evaluation framework, which is needed to quantitatively assess the practical benefits that effective explainability should offer practitioners. Here, we hypothesize that superior attention maps, as a mechanism of model explanation, should align with the information that physicians focus on, potentially reducing prediction uncertainty and increasing model reliability. We employed a multimodal transformer to predict lymph node metastasis of rectal cancer using clinical data and magnetic resonance imaging. We explored how well attention maps, visualized through a state-of-the-art technique, can achieve agreement with physician understanding. Subsequently, we compared two distinct approaches for estimating uncertainty: a standalone estimation using only the variance of prediction probability, and a human-in-the-loop estimation that considers both the variance of prediction probability and the quantified agreement. Our findings revealed no significant advantage of the human-in-the-loop approach over the standalone one. In conclusion, this case study did not confirm the anticipated benefit of the explanation in enhancing model reliability. Superficial explanations could do more harm than good by misleading physicians into relying on uncertain predictions, suggesting that the current state of attention mechanisms should not be overestimated in the context of model explainability.

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

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.

Sketch-based semantic retrieval of medical images.

The volume of medical images stored in hospitals is rapidly increasing; however, the utilization of these accumulated medical images remains limited. Existing content-based medical image retrieval (CBMIR) systems typically require example images, leading to practical limitations, such as the lack of customizable, fine-grained image retrieval, the inability to search without example images, and difficulty in retrieving rare cases. In this paper, we introduce a sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images. The key concept is feature decomposition of medical images, which allows the entire feature of a medical image to be decomposed into and reconstructed from normal and abnormal features. Building on this concept, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. The system integrates both types of input to construct a query vector and retrieves reference images. For evaluation, ten healthcare professionals participated in a user test using two datasets. Consequently, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for rare cases. Our SBMIR system provides on-demand, customizable medical image retrieval, thereby expanding the utility of medical image databases.

Characteristics of false-positive lesions in evaluating colorectal liver metastases on gadoxetic acid-enhanced magnetic resonance imaging.

PURPOSE: Gadoxetic acid-enhanced MRI (Gd-EOB-MRI) shows higher sensitivity for colorectal liver metastases (CRLM) than contrast-enhanced computed tomography (CECT). However, the details of false-positive lesions for each imaging modality are unknown. METHODS: Cases undergoing hepatectomy for CRLM following a preoperative evaluation with both CECT and Gd-EOB-MRI between July 2008 and December 2016 were reviewed. The false-positive and false-negative rates were assessed for each modality, and the characteristics of false-positive lesions were evaluated. RESULTS: We evaluated 275 partial hepatectomies in 242 patients without preoperative chemotherapy. Among the 275 hepatectomies, 546 lesions were recognized by CECT and/or Gd-EOB-MRI. The false-positive rates for CECT and Gd-EOB-MRI were 4% (18/422) and 7% (37/536), respectively. The size of false-positive lesions was significantly smaller than that of correctly diagnosed lesions (median: 28 mm [3-120 mm] vs 7.6 mm [320 mm], P < 0.001). Compared with the 233 correctly diagnosed lesions ≤ 20 mm in diameter, false-positive lesions were more frequently located near the liver surface or vasculobiliary structures than true lesions (33/37 [89%] vs 149/233 [64%], respectively; P = 0.0021). CONCLUSION: Gd-EOB-MRI had a 7% false-positive rate. A small size and tumor location near the surface or near vasculobiliary structures were associated with false positivity.

Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging.

In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians often need corresponding images without abnormal findings of interest or, conversely, images that contain similar abnormal findings regardless of normal anatomical context. This is called comparative diagnostic reading of medical images, which is essential for a correct diagnosis. To support comparative diagnostic reading, content-based image retrieval (CBIR) that can selectively utilize normal and abnormal features in medical images as two separable semantic components will be useful. In this study, we propose a neural network architecture to decompose the semantic components of medical images into two latent codes: normal anatomy code and abnormal anatomy code. The normal anatomy code represents counterfactual normal anatomies that should have existed if the sample is healthy, whereas the abnormal anatomy code attributes to abnormal changes that reflect deviation from the normal baseline. By calculating the similarity based on either normal or abnormal anatomy codes or the combination of the two codes, our algorithm can retrieve images according to the selected semantic component from a dataset consisting of brain magnetic resonance images of gliomas. Moreover, it can utilize a synthetic query vector combining normal and abnormal anatomy codes from two different query images. To evaluate whether the retrieved images are acquired according to the targeted semantic component, the overlap of the ground-truth labels is calculated as metrics of the semantic consistency. Our algorithm provides a flexible CBIR framework by handling the decomposed features with qualitatively and quantitatively remarkable results.

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals.

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

Observing deep radiomics for the classification of glioma grades.

Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, a technical challenge lies in the difficulty of determining which types of internal representation are associated with a specific task, because feature vectors can vary dynamically according to individual inputs. Here, based on the magnetic resonance imaging (MRI) of gliomas, we propose a novel method to extract a shareable set of feature vectors that encode various parts in tumor imaging phenotypes. By applying vector quantization to latent representations, features extracted by an encoder are replaced with a fixed set of feature vectors. Hence, the set of feature vectors can be used in downstream tasks as imaging markers, which we call deep radiomics. Using deep radiomics, a classifier is established using logistic regression to predict the glioma grade with 90% accuracy. We also devise an algorithm to visualize the image region encoded by each feature vector, and demonstrate that the classification model preferentially relies on feature vectors associated with the presence or absence of contrast enhancement in tumor regions. Our proposal provides a data-driven approach to enhance the understanding of the imaging appearance of gliomas.

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities.

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient's quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

Circumferential Resection Margin Status as a Predictive Factor for Recurrence in Preoperative MRI for Advanced Lower Rectal Cancer Without Preoperative Therapy.

BACKGROUND: In Japan, total mesorectal excision plus lateral lymph node dissection without preoperative therapy is the standard treatment for advanced lower rectal cancer. Although long-term oncologic outcomes with preoperative therapy based on circumferential resection margin status in preoperative MRI has been reported, outcomes without preoperative therapy are unknown. OBJECTIVE: This study evaluated long-term oncologic outcomes of radical surgery without preoperative therapy in advanced lower rectal cancer based on circumferential resection margin status in preoperative MRI, with the aim of defining appropriate patient populations for preoperative therapy. DESIGN: This retrospective analysis compared long-term oncologic outcomes with preoperative MRI in patients with lower rectal cancer. SETTINGS: Patients were identified through a database managed by our institute. PATIENTS: In total, 338 patients with lower rectal cancer who underwent radical surgery between 2000 and 2014 at the National Cancer Center Hospital without preoperative therapy were included. MAIN OUTCOME MEASURES: The main outcome was relapse-free survival. RESULTS: The median follow-up period was 61.7 months (range, 3-153 months). Five-year relapse-free survival rates in MRI-predicted circumferential resection margin negative patients and positive patients were 76.0% and 55.6% (p < 0.001). Univariate and multivariate analyses revealed pN stage (HR, 2.35; 95% CI, 1.470-3.770; p < 0.001), lymphatic invasion (HR, 2.03; 95% CI, 1.302-3.176; p = 0.002), venous invasion (HR, 2.15; 95% CI, 1.184-3.9; p = 0.01), surgical procedure (HR, 1.72; 95% CI, 1.115-2.665; p = 0.01), and MRI-predicted circumferential resection margin (HR, 1.850; 95% CI, 1.206-2.838; p = 0.0051) to be independent risk factors for postoperative recurrence. LIMITATIONS: This study was retrospective in design. CONCLUSIONS: Magnetic resonance imaging-predicted circumferential resection margin was associated with relapse-free survival without preoperative therapy, indicating its potential for use in selecting optimal preoperative therapy. See Video Abstract at http://links.lww.com/DCR/B335. ESTADO DEL MARGEN DE RESECCIÓN CIRCUNFERENCIAL COMO FACTOR PREDICTIVO DE RECURRENCIA EN LA RESONANCIA MAGNÉTICA PREOPERATORIA, PARA EL CÁNCER RECTAL BAJO AVANZADO SIN TERAPIA PREOPERATORIA: En Japón, la escisión mesorrectal total con disección de ganglios linfáticos laterales y sin terapia preoperatoria, es el tratamiento estándar para el cáncer rectal bajo avanzado. Aunque se han reportado resultados oncológicos a largo plazo con terapia preoperatoria, basada en el estado del margen de resección circunferencial en la resonancia magnética preoperatoria, se desconocen los resultados sin terapia preoperatoria.Este estudio evaluó los resultados oncológicos a largo plazo de cirugía radical sin terapia preoperatoria, en cáncer rectal bajo avanzado, basado en el estado del margen de resección circunferencial en la resonancia magnética preoperatoria, con el objetivo de definir poblaciones de pacientes apropiadas para terapia preoperatoria.Este análisis retrospectivo comparó los resultados oncológicos a largo plazo con resonancia magnética preoperatoria, en pacientes con cáncer rectal bajo.Los pacientes fueron identificados a través de una base de datos administrada por nuestro instituto.Se incluyeron un total de 338 pacientes con cáncer rectal bajo, que se sometieron a cirugía radical entre 2000 y 2014 en el Hospital Nacional del Centro de Cáncer, sin terapia preoperatoria.El resultado principal fue la supervivencia libre de recaídas.La mediana del período de seguimiento fue de 61,7 meses (rango, 3-153 meses). Las tasas de supervivencia sin recaídas a cinco años, con margen de resección circunferencial predicho por resonancia magnética, en pacientes negativos y pacientes positivos fueron 76.0% y 55.6% (p <0.001), respectivamente. Los análisis univariados y multivariados revelaron estadio pN (razón de riesgo [HR], 2.35; intervalo de confianza [IC] del 95%, 1.470-3.770; p <0.001), invasión linfática (HR, 2.03; IC del 95%, 1.302-3.176; p = 0.002), invasión venosa (HR, 2.15; IC 95%, 1.184-3.9; p = 0.01), procedimiento quirúrgico (HR, 1.72; IC 95%, 1.115-2.665; p = 0.01) y circunferencial predicho por resonancia magnética en margen de resección (HR, 1.850; IC 95%, 1.206-2.838; p = 0.0051), como factores de riesgo independientes, para la recurrencia postoperatoria.Este estudio fue retrospectivo en diseño.El margen de resección circunferencial predicho de resonancia magnética, se asoció con una supervivencia libre de recaída sin terapia preoperatoria, lo que indica su potencial para uso en la selección de la terapia óptima preoperatoria. Consulte Video Resumen en http://links.lww.com/DCR/B335.

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Preoperative T staging of colon cancer using CT colonography with multiplanar reconstruction: new diagnostic criteria based on "bordering vessels".

PURPOSE: Preoperative T staging of colon cancer, in particular, for distinguishing T3 from T2 and T4, has been a challenge. The aim of this study was to evaluate newly developed criteria for preoperative T staging of colorectal cancer using computed tomography colonography (CTC) with multiplanar reconstruction (MPR), based on the spatial relationship of tumors and "bordering vessels," that is, marginal vessels that are detectable by multi-detector row CT with MPR. METHODS: A total of 172 patients with colon and upper rectal cancer who underwent preoperative CTC and surgery between August 2011 and September 2013 were included. Preoperative T staging using the new criteria was performed prospectively and compared with pathologic results. RESULTS: Sensitivity, specificity, and accuracy of T staging by CTC using the new criteria were 63%, 80%, and 77% for T2 (n = 30); 72%, 94%, and 81% for T3 (n = 95); and 79%, 99%, and 97% for T4a (n = 14), respectively. Positive predictive value for T3 was 93%, indicating that a T3 diagnosis by CTC is precise. In addition, negative predictive value for pathological T4a was 98%, indicating that a "not T4a" diagnosis by CTC is also precise. CONCLUSIONS: Our newly developed criteria are useful for preoperative T staging, particularly for distinguishing T3 from T2 and T4.

Computed tomographic colonography with a reduced dose of laxative using a novel barium sulfate contrast agent in Japan.

PURPOSE: To test the tagging efficacy, patient acceptability, and accuracy of computed tomographic colonography (CTC) with a reduced dose of laxative using a novel barium sulfate (BaSO4) contrast agent. MATERIALS AND METHODS: CTC followed by optical colonoscopy (OC) was performed on 73 patients with positive results in fecal occult blood tests. They were administrated a BaSO4 suspension and a magnesium citrate solution for bowel preparation. Patients completed a questionnaire about the acceptability of bowel preparation. Tagging efficacy was estimated using a novel categorization system, which classified all segments into 8 categories. The accuracy of detecting protruded lesions ≥ 6 mm was calculated from the comparison of CTC and OC results, using the latter as a reference standard. RESULTS: Tagging efficacy was good in 77.3% of colonic segments where residue was observed. The acceptability of bowel preparation for CTC was significantly higher than that for OC. The sensitivity, specificity, and positive and negative predictive values were 0.778, 0.945, 0.824, and 0.929, respectively. All lesions ≥ 7 mm were successfully detected by CTC. CONCLUSION: CTC with a reduced dose of laxative using a novel BaSO4 contrast agent has a favorable tagging efficacy, patient acceptability, and accuracy.

Intersphincteric Resection Has Similar Long-term Oncologic Outcomes Compared With Abdominoperineal Resection for Low Rectal Cancer Without Preoperative Therapy: Results of Propensity Score Analyses.

BACKGROUND: Intersphincteric resection has been performed for very low rectal cancer in place of abdominoperineal resection to avoid permanent colostomy. OBJECTIVE: This study aimed to evaluate long-term oncologic outcomes of intersphincteric resection compared with abdominoperineal resection. DESIGN: In this retrospective study, propensity score matching and stratification analyses were performed to reduce the effects of confounding factors between groups, including age, sex, BMI, CEA value, tumor height, tumor depth, lymph node enlargement, and circumferential resection margin measured by MRI. SETTING: A database maintained at our institute was used to identify patients during the period between 2000 and 2014. PATIENTS: A total of 285 patients who underwent curative intersphincteric resection (n = 112) or abdominoperineal resection (n = 173) for stage I to III low rectal cancer without preoperative chemoradiotherapy were enrolled in this study. MAIN OUTCOME MEASURE: The main outcome was recurrence-free survival. RESULTS: Patients in the abdominoperineal resection group were more likely to have a preoperative diagnosis of advanced cancer before case matching. After case matching, clinical outcomes were similar between intersphincteric resection and abdominoperineal resection groups. Five-year relapse-free survival rates were 69.9% for the intersphincteric resection group and 67.9% for abdominoperineal resection group (p = 0.64), and were similar in the propensity score-matched cohorts (89 matched pairs). Three-year cumulative local recurrence rates were 7.3% for intersphincteric resection and 3.9% for abdominoperineal resection (p = 0.13). In the propensity score-matched model, the hazard ratio for recurrence after intersphincteric resection in comparison with abdominoperineal resection was 0.90. Stratification analysis revealed similar recurrence rates (HR, 0.75-1.68) for intersphincteric resection in comparison with abdominoperineal resection. LIMITATION: Eight covariates were incorporated into the model, but other covariates were not included. CONCLUSIONS: Our findings suggest similar oncologic outcomes for intersphincteric resection and abdominoperineal resection without preoperative chemoradiotherapy in patients with low rectal cancer adjusted for background variables. See Video Abstract at http://links.lww.com/DCR/A661.

Efficacy and Safety of Pazopanib for Recurrent or Metastatic Solitary Fibrous Tumor.

OBJECTIVE: To investigate the efficacy and safety of pazopanib for recurrent or metastatic solitary fibrous tumor (SFT) in first- and second-line settings. METHODS: Patients histologically diagnosed with SFT at our hospital who received pazopanib monotherapy for inoperable disease between January 2013 and November 2016 were eligible. We retrospectively investigated treatment outcomes according to the treatment lines and assessed adverse events. RESULTS: Nine patients were eligible. The median age was 67 years (range 42-81), and 6 patients (66.7%) were male. Four patients (50%) received pazopanib as second-line treatment. According to the RECIST and Choi criteria, the respective response rates were 0 and 50%, while the respective disease control rates were 88.9 and 75%. The median progression-free survival (PFS) was 6.2 months (95% confidence interval 3.2-8.8). Treatment line and high frequency of mitosis were not predictive of PFS (p = 0.67, 0.92). Two patients (22.2%) experienced elevated liver enzymes of grade 3 or higher. CONCLUSION: Pazopanib is an effective treatment option for recurrent or metastatic SFT in first- and second-line settings. Liver injury is a major adverse event and adequate treatment withdrawal and dose reduction should be considered when necessary.

Minute mesenchymal chondrosarcoma within osteochondroma: an unexpected diagnosis confirmed by HEY1-NCOA2 fusion.

Mesenchymal chondrosarcoma is rare and can be challenging to diagnose. Herein, we report a minute mesenchymal chondrosarcoma within an osteochondroma. A 12-year-old girl presented with an asymptomatic exophytic lesion of the rib. The tumor was clinically diagnosed as osteochondroma and was excised after observation for 3 years. The resected specimen revealed an unexpected minute (0.9 cm) "monophasic" mesenchymal chondrosarcoma in the apex of the lesion. The sarcoma consisted of monomorphic spindle cells without hyaline cartilage. Fluorescence in situ hybridization detected NCOA2 rearrangement, and reverse-transcription polymerase chain reaction and sequencing detected a HEY1 (exon 4)-NCOA2 (exon 13) fusion transcript. The patient did not receive adjuvant therapy and is alive with no recurrence 6 years after surgery. The present case highlights the value of careful pathological examination of specimens submitted as osteochondroma and emphasizes the usefulness of molecular assays in the diagnosis of mesenchymal chondrosarcoma in an atypical setting.