Characteristics of Patients Presented With Metastases During or After Completion of Chemoradiation Therapy for Locally Advanced Rectal Cancer: A Case Series

Purpose Locally advanced rectal cancer (LARC) is managed by chemoradiotherapy (CRT), followed by surgery. Herein we reported patients with metastases during or after CRT. Methods Data of patients with LARC who received CRT from 2008 to 2017 were reviewed. Patients with metastases after CRT were included. Those with metastatic tumors at the initial diagnosis were excluded. Results Fourteen patients (1.3%) of 1,092 who received CRT presented with metastases. Magnetic resonance circumferential resection margin (mrCRM) and mesorectal lymph nodes (LNs) were positive in 12 patients (85.7%). Meanwhile, magnetic resonance extramural vascular invasion (mrEMVI) was positive in 10 patients (71.4%). Magnetic resonance tumor regression grade (mrTRG) 4 and mrTRG5 was detected in 5 and 1 patient respectively. Ten patients (71.4%) underwent combined surgery and 3 (21.4%) received palliative chemotherapy. Conclusion Patients with metastases after CRT showed a higher rate of positive mrCRM, mrEMVI, mesorectal LNs, and poor tumor response. Further studies with a large number of patients are necessary for better survival outcomes in LARC.ope

Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.ope

Effectiveness of Hepatocellular Carcinoma Surveillance and an Optimal Surveillance Interval: Nationwide Cohort of Korea

Purpose: To assess associations between surveillance intervals in a national hepatocellular carcinoma (HCC) surveillance program and receiving curative treatment and mortality using nationwide cohort data for Korea. Materials and methods: Using the National Health Insurance Service Database of Korea, we retrospectively identified 3201852 patients, the target population of the national HCC surveillance program, between 2008 and 2017. After exclusion, a total of 64674 HCC patients were divided based on surveillance intervals: never screened, ≤6 months (6M), 7-12 months (1Y), 13-24 months (2Y), and 25-36 months (3Y). Associations for surveillance interval with the chance to receive curative therapy and all-cause mortality were analyzed. Results: The 6M group (51.9%) received curative therapy more often than the other groups (1Y, 48.3%; 2Y, 43.8%; 3Y, 41.3%; never screened, 34.5%). Odds ratio for receiving curative therapy among the other surveillance interval groups (1Y, 0.87; 2Y, 0.76; 3Y, 0.77; never screened, 0.57; p<0.001) were significantly lower than that of the 6M group. The hazard ratios (HRs) of all-cause mortality were 1.07, 1.14, and 1.37 for 2Y, 3Y, and never screened groups. The HR for the 1Y group (0.96; p=0.092) was not significantly different, and it was lower (0.91; p<0.001) than that of the 6M group after adjustment for lead-time bias. Curative therapy was associated with survival benefits (HR, 0.26; p<0.001). Conclusion: HCC surveillance, especially at a surveillance interval of 6 months, increases the chance to receive curative therapy.ope

Development of a multi-channel NIRS-USG hybrid imaging system for detecting prostate cancer and improving the accuracy of imaging-based diagnosis: a phantom study

PURPOSE: This study aimed to develop a multi-channel near-infrared spectroscopy (NIRS) and ultrasonography (USG) fusion imaging system for imaging prostate cancer and to verify its diagnostic capability by applying the hybrid imaging system to a prostate cancer phantom. METHODS: A multi-channel NIRS system using the near-infrared 785-nm wavelength with 12 channels and four detectors was developed. After arranging the optical fibers around a USG transducer, we performed NIRS imaging and grayscale USG imaging simultaneously. Fusion imaging was obtained by processing incoming signals and the spatial reconstruction of NIRS, which corresponded with grayscale USG acquired at the same time. The NIRS-USG hybrid system was applied to a silicone-based optical phantom of the prostate gland containing prostate cancer to verify its diagnostic capability qualitatively. RESULTS: The NIRS-USG hybrid imaging system for prostate cancer imaging simultaneously provided anatomical and optical information with 2-dimensional registration. The hybrid imaging system showed more NIR attenuation over the prostate cancer model than over the model of normal prostate tissue. Its diagnostic capability to discriminate a focal area mimicking the optical properties of prostate cancer from the surrounding background mimicking the optical properties of normal prostate tissue was verified by applying the hybrid system to a silicone-based optical phantom of prostate cancer. CONCLUSION: This study successfully demonstrated that the NIRS-USG hybrid system may serve as a new imaging method for improving the diagnostic accuracy of prostate cancer, with potential utility for future clinical applications.ope

Extraosseous Ewing’s Sarcoma Presented as a Rectal Subepithelial Tumor: Radiological and Pathological Features

Purpose: Extraosseous Ewing’s sarcoma (EOE) of the rectum is extremely rare: only three cases have been reported in the literature and none of these reports described their imaging findings in detail. Herein, we describe the tumor imaging and pathological features in detail. Materials and Methods: We report a case of rectal EOE in a 72-year-old female who received local excision and was provisionally diagnosed with a rectal submucosal spindle cell tumor. We used immunohistochemistry, histopathology, and fluorescence in situ hybridization to characterize the tumor and provide a definitive diagnosis of EOE. Results: MRI revealed a well-demarcated submucosal tumor with heterogeneous enhancement and hemorrhagic foci in rectum. EOE was diagnosed by positive staining of tumor cells for CD99 and Fli-1 by immunohistochemistry and the presence of the EWSR1 gene translocation by fluorescence in situ hybridization. Although the patient underwent radiation treatment and surgery, the tumor recurred after 4 months as revealed by computed tomography and magnetic resonance imaging. Conclusion: Rectal EOE may present as a rectal submucosal tumor. The understanding of imaging and histological characteristics of this tumor are critical for accurate diagnosis and appropriate aggressive treatment.ope

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.ope

MR prediction of pathologic complete response and early-stage rectal cancer after neoadjuvant chemoradiation in patients with clinical T1/T2 rectal cancer for organ saving strategy

To evaluate the ability of magnetic resonance imaging (MRI) to predict pathologic complete response (pCR) after neoadjuvant chemoradiation therapy (CRT) in patients with clinical T1/T2 rectal cancer to indicate candidates for organ-saving strategies.Between 2012 and 2016, 38 patients with clinical T1/T2 rectal cancer received neoadjuvant CRT. Radiologic complete response (rCR) was assigned when dense fibrotic tissue without tumor signal intensity was observed on post-CRT MRI. Surgical pathologic assessment was used to evaluate tumor regression. The association between rCR and the mural extent of the primary tumor, pCR, and pathologic T stage were analyzed.In rCR patients, the pCR rate was higher; the odds of achieving pCR were 8.00 times higher than for non-rCR patients (P = .02). rCR patients were also more likely to have early-stage cancer than non-rCR patients (P = 0.01). Patients with partial extent of the primary tumor on post-CRT MRI were more likely to be diagnosed with early-stage cancer than those with transmural extent (P = .01).rCR indicated by post-CRT MRI can be used as a supportive factor to predict pCR after neoadjuvant CRT in patients with clinical T1/T2 rectal cancer and can guide management decisions around organ-saving treatments.ope

Classification of focal liver lesions in CT images using convolutional neural networks with lesion information augmented patches and synthetic data augmentation

Purpose: We propose a deep learning method that classifies focal liver lesions (FLLs) into cysts, hemangiomas, and metastases from portal phase abdominal CT images. We propose a synthetic data augmentation process to alleviate the class imbalance and the Lesion INformation Augmented (LINA) patch to improve the learning efficiency. Methods: A dataset of 502 portal phase CT scans of 1,290 FLLs was used. First, to alleviate the class imbalance and to diversify the training data patterns, we suggest synthetic training data augmentation using DCGAN-based lesion mask synthesis and pix2pix-based mask-to-image translation. Second, to improve the learning efficiency of convolutional neural networks (CNNs) for the small lesions, we propose a novel type of input patch termed the LINA patch to emphasize the lesion texture information while also maintaining the lesion boundary information in the patches. Third, we construct a multi-scale CNN through a model ensemble of ResNet-18 CNNs trained on LINA patches of various mini-patch sizes. Results: The experiments demonstrate that (a) synthetic data augmentation method shows characteristics different but complementary to those in conventional real data augmentation in augmenting data distributions, (b) the proposed LINA patches improve classification performance compared to those by existing types of CNN input patches due to the enhanced texture and boundary information in the small lesions, and (c) through an ensemble of LINA patch-trained CNNs with different mini-patch sizes, the multi-scale CNN further improves overall classification performance. As a result, the proposed method achieved an accuracy of 87.30%, showing improvements of 10.81%p and 15.0%p compared to the conventional image patch-trained CNN and texture feature-trained SVM, respectively. Conclusions: The proposed synthetic data augmentation method shows promising results in improving the data diversity and class imbalance, and the proposed LINA patches enhance the learning efficiency compared to the existing input image patches.restrictio

대장암 환자에서 발견되는 국소 간병변의 감별을 위한 조영증강 전산화 단층촬영에서의 라디오믹스 연구

Objective: To evaluate the diagnostic performance of a radiomics model for classifying hepatic cysts, hemangiomas, and metastases in patients with colorectal cancer (CRC) from portal-phase abdominopelvic computed tomography (CT) images. Methods: This retrospective study included 502 patients with CRC who underwent both contrast-enhanced abdominopelvic CT and contrast-enhanced liver magnetic resonance imaging between January 2005 and December 2010. Patients were divided into training (n = 386) and validation (n = 116) cohorts. Portal-phase contrast-enhanced CT images of 1290 liver lesions (size range, 3 mm–5 cm) were used to develop a radiomics model for differentiating three classes of liver lesions (cyst, hemangioma, and metastasis). Among multiple handcrafted features, the feature selection was performed using the ReliefF method, and random forest classifiers were used to train the selected features. The diagnostic performance of the developed model was evaluated and compared with that of four radiologists who classified liver lesions from the validation cohort (128 cysts, 30 hemangiomas, and 149 metastases). Additionally, a subgroup analysis was conducted based on lesion size (<10 mm or ≥10 mm). Results: The radiomics model demonstrated significantly lower overall and hemangioma- and metastasis-specific polytomous discrimination index (PDI) (overall PDI, 0.8037; hemangioma-specific PDI, 0.6653; metastasis-specific PDI, 0.8027) than the radiologists’ results, except that of the least-experienced radiologist (overall PDI, 0.9622–0.9680; hemangioma-specific PDI, 0.9452–0.9630; metastasis-specific PDI, 0.9511–0.9869). For differentiating subcentimeter lesions, the PDI of the radiomics model was different according to the lesion size (overall PDI of < 10 mm, 0.6486; overall PDI of ≥ 10 mm, 0.8264; p-value, 0.0692) while that of the radiologists was relatively maintained. For classifying benign lesions from metastasis, the radiomics model showed excellent diagnostic performance, with an accuracy of 84.36% (78.59–88.8) and an area under the receiver operating characteristic curve of 0.9426 (0.9149–0.9703). However, the three most experienced radiologists outperformed the radiomics model with an accuracy of 93.81–96.09% (p-value, 0.002–0.003). Conclusion: The radiomics model achieved diagnostic accuracy comparable to that of radiologists when differentiating cysts, hemangiomas, and metastases from portal-phase CT images of patients with CRC and demonstrated potential for clinical use. However, this model was limited particularly to classifying hemangiomas and subcentimeter liver lesions, and therefore, unattended application of the system in daily clinical practice is not yet feasible. 목적: 대장암에서 간은 제일 흔하게 전이가 되는 장기로, 대장암 환자에서 간전이를 빠르게 진단하고, 간 병변을 정확하게 감별하는 것은 중요하다. 따라서, 본 연구에서는 조영증강 복부 전산화 단층촬영의 문맥기 영상에서 간 낭종, 혈관종 및 간 전이를 분류할 수 있는 라디오믹스 모델을 구축하고, 진단 정확도를 평가하고자 하였다. 방법: 2005년 1월부터 2010년 12월에 조영증강 복부 전산화 단층촬영과 조영증강 간 자기공명영상을 모두 시행한 502명의 대장암 환자를 후향적으로 분석하였으며, 2006년 8월 23일을 기준으로 그 이후에 조영증강 자기공명영상을 시행 받은 386명의 환자를 학습군으로 하였고, 나머지 116명의 환자를 검증군으로 포함하였다. 502명의 환자에서 병변의 크기가 3mm 이상, 5cm 이하인 간 낭종, 혈관종, 간 전이 병변이 포함이 되었으며, 총 1290개의 간 병변 (간 낭종 676개, 혈관종 130개, 간 전이 484개)의 문맥기 조영증강 전산화 단층촬영 영상을 이용하여 3 군의 간 병변을 분류할 수 있는 라디오믹스 모델을 구축하였다. 추출해낸 129개의 사람이 정의한 특징 (hand-crafted feature)중에서 관찰자간 재현성이 우수한 (intraclass correlation coefficient> 0.75) 126개의 특징만을 모델링에 사용하였다. ReliefF 방법을 통하여 특징 선택 (feature selection)을 시행하였으며, 선택된 특징들은 랜덤 포레스트를 이용하여 학습시킨 뒤에 최종적으로 라디오믹스 모델을 구축하였다. 구축한 모델의 진단능은 검증군에 포함된 간 병변을 이용하여 평가하였으며, 4명의 영상의학과 의사와 진단 정확도를 비교하였다. 이와 더불어 병변의 크기를 기준으로 10mm 미만과 10mm 이상으로 나누어서 추가적으로 분석을 시행하였다. 3군의 간 병변을 분류하는 진단능을 비교하기 위해서 polytomous discrimination index (PDI)와 correct classification percentage (CCP)를 구하였고, 양성 (간 낭종과 혈관종)과 악성의 이항 분류 진단능을 비교하기 위해서 민감도, 특이도, 양성 예측도, 음성 예측도, 진단 정확도 및 수신자 조작 특성 곡선 (receiver operating characteristic curve)의 곡선 아래 면적 (area under the curve)을 분석하였다. 결과: 라디오믹스 모델의 전체 PDI, 혈관종 PDI 및 간 전이 PDI는 각각 0.8037, 0.6653, 0.8027로 측정되었으며, 해당 수치는 가장 경험이 없는 영상의학과 의사를 제외한 모든 영상의학과 의사 (전체 PDI, 0.9622-0.9680; 혈관종 PDI, 0.9452-0.9630; 간 전이 PDI, 0.9511-0.9869)에 비해서 유의미하게 낮은 결과였다. 특히나, 병변에 상관없이 군 별 PDI가 비슷하게 높은 영상의학과 의사와 비교하여, 라디오믹스 모델의 혈관종 PDI는 0.6653으로 다른 군 별 PDI에 비해 낮은 수치로 측정되었다. 10mm 이상의 병변을 평가할 때와 비교하여 10mm미만의 병변을 감별할 때 라디오믹스 모델의 PDI는 감소하였으나, 영상의학과 의사의 PDI는 병변의 크기에 상관이 없이 비교적 유지가 되는 결과를 보여주었다. 이항 분류에서는 라디오믹스 모델이 좋은 진단능을 보여주었으며, 간 전이를 감별하는 진단 정확도가 84.36% (95% 신뢰도, 78.59-88.8), 수신자 조작 특성 곡선의 곡선 아래 면적이 0.9426 (0.9149-0.9703)로 측정되었다. 그러나, 제일 경험이 적은 영상의학과 전공의를 제외하고 나머지 3명의 영상의학과 의사는 이항 분류에 있어서도 라디오믹스 모델을 뛰어 넘는 결과를 보여주었으며, 진단 정확도는 93.81%-96.09%로 측정되었고, 모델과의 차이는 통계학적으로 유의미한 차이였다 (p-value, 0.002-0.003). 결론: 라디오믹스 모델은 대장암 환자의 문맥기 조영증강 전산화 단층촬영 영상에서 간 낭종, 혈관종과 간 전이를 분류하는데 있어서 영상의학과 의사보다는 낮지만, 비교적 좋은 진단 정확도를 보여, 임상적으로 적용이 될 수 있는 잠재력을 보여주었다. 하지만, 라디오믹스 모델은 혈관종과 10mm 미만의 병변을 진단하는 것에 있어 한계를 보여 실제 진료 환경에서 사람의 개입 없이 적용이 되기에는 아직은 시기 상조로 판단된다open박

PI-RADS version 2: optimal time range for determining positivity of dynamic contrast-enhanced MRI in peripheral zone prostate cancer

Aim: To analyse the optimal time cut-off for determining positivity of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in peripheral zone (PZ) prostate cancer (PCa). Materials and methods: A consecutive series of 89 patients with PZ PCa who had undergone diffusion-weighted imaging (DWI) and subtraction DCE MRI were included. An experienced reader visually analysed the earliest time after contrast medium injection to visualise the best contrast between an index tumour and normal PZ on DCE MRI (i.e., best contrast time). The best contrast time cut-off for clinically significant cancer (csPCa) according to Epstein criteria or International Society of Urological Pathology (ISUP) grade ≥2 was analysed by an experienced reader, and applied to a less-experienced reader. For the index lesion of DWI category 3, the added value of DCE MRI (increased true positive and negative rates of PI-RADSv2 for csPCa) was evaluated using the cut-off time. Results: The best contrast time cut-off for csPCa was ≤72 seconds for Epstein criteria and ≤56 seconds for ISUP grade ≥2 by an experienced reader. The weighted kappa to determine positivity of DCE MRI was 0.622 for ≤72 seconds and 0.527 for ≤56 seconds between the two readers. The added value of DCE MRI was 55-75% by an experienced reader and 39.1-69.6% by a less-experienced reader. Conclusion: For interpreting PI-RADSv2, imaging findings within 60-72 seconds following contrast media injection seem to reliably determine positivity of DCE MRI in PZ, and have added value for detecting csPCa.restrictio