Three-dimensional convolutional neural network-based classification of chronic kidney disease severity using kidney MRI.

A three-dimensional convolutional neural network model was developed to classify the severity of chronic kidney disease (CKD) using magnetic resonance imaging (MRI) Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) imaging. Seventy-three patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m2, CKD stage G4-5); 172 with moderate renal dysfunction (30 ≤ eGFR < 60 mL/min/1.73 m2, CKD stage G3a/b); and 76 with mild renal dysfunction (eGFR ≥ 60 mL/min/1.73 m2, CKD stage G1-2) participated in this study. The model was applied to the right, left, and both kidneys, as well as to each imaging method (T1-weighted IP/OP/WO images). The best performance was obtained when using bilateral kidneys and IP images, with an accuracy of 0.862 ± 0.036. The overall accuracy was better for the bilateral kidney models than for the unilateral kidney models. Our deep learning approach using kidney MRI can be applied to classify patients with CKD based on the severity of kidney disease.

Evaluation of Uterine Carcinosarcoma and Uterine Endometrial Carcinoma Using Magnetic Resonance Imaging Findings and Texture Features.

Aim  This study aimed to evaluate the diagnostic feasibility of magnetic resonance imaging (MRI) findings and texture features (TFs) for differentiating uterine endometrial carcinoma from uterine carcinosarcoma. Methods This retrospective study included 102 patients who were histopathologically diagnosed after surgery with uterine endometrial carcinoma (n=68) or uterine carcinosarcoma (n=34) between January 2008 and December 2021. We assessed conventional MRI findings and measurements (cMRFMs) and TFs on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) map, as well as their combinations, in differentiating between uterine endometrial carcinoma and uterine carcinosarcoma. The least absolute shrinkage and selection operator (LASSO) was used to select three features with the highest absolute value of the LASSO regression coefficient for each model and construct a discriminative model. Binary logistic regression analysis was used to analyze the disease models and conduct receiver operating characteristic analyses on the cMRFMs, T2WI-TFs, ADC-TFs, and their combined model to compare the two diseases. Results A total of four models were constructed from each of the three selected features. The area under the curve (AUC) of the discriminative model using these features was 0.772, 0.878, 0.748, and 0.915 for the cMRFMs, T2WI-TFs, ADC-TFs, and a combined model of cMRFMs and TFs, respectively. The combined model showed a higher AUC than the other models, with a high diagnostic performance (AUC=0.915). Conclusion A combined model using cMRFMs and TFs might be helpful for the differential diagnosis of uterine endometrial carcinoma and uterine carcinosarcoma.

A case of ovarian endometrioid carcinoma: Atypical MR imaging.

Endometrioid carcinoma is the second most common ovarian tumor, classified as an epithelial-stromal ovarian tumor, and is usually characterized by a cystic tumor with partial solid components on magnetic resonance (MR) images. In this case report, we discuss an 81-year-old female who presented with atypical genital bleeding and distended abdomen, for which she underwent abdominal computed tomography and MR imaging. Solid endometrioid carcinoma of the ovary is very rare but was confirmed in our patient during the histological examination after surgery.

The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network.

We developed a 3D convolutional neural network (CNN)-based automatic kidney segmentation method for patients with chronic kidney disease (CKD) using MRI Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images. The dataset comprised 100 participants with renal dysfunction (RD; eGFR < 45 mL/min/1.73 m2) and 70 without (non-RD; eGFR ≥ 45 mL/min/1.73 m2). The model was applied to the right, left, and both kidneys; it was first evaluated on the non-RD group data and subsequently on the combined data of the RD and non-RD groups. For bilateral kidney segmentation of the non-RD group, the best performance was obtained when using IP image, with a Dice score of 0.902 ± 0.034, average surface distance of 1.46 ± 0.75 mm, and a difference of - 27 ± 21 mL between ground-truth and automatically computed volume. Slightly worse results were obtained for the combined data of the RD and non-RD groups and for unilateral kidney segmentation, particularly when segmenting the right kidney from the OP images. Our 3D CNN-assisted automatic segmentation tools can be utilized in future studies on total kidney volume measurements and various image analyses of a large number of patients with CKD.

The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model.

We evaluated a multiclass classification model to predict estimated glomerular filtration rate (eGFR) groups in chronic kidney disease (CKD) patients using magnetic resonance imaging (MRI) texture analysis (TA). We identified 166 CKD patients who underwent MRI comprising Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images, apparent diffusion coefficient (ADC) maps, and T2* maps. The patients were divided into severe, moderate, and control groups based on eGFR borderlines of 30 and 60 mL/min/1.73 m2. After extracting 93 texture features (TFs), dimension reduction was performed using inter-observer reproducibility analysis and sequential feature selection (SFS) algorithm. Models were created using linear discriminant analysis (LDA); support vector machine (SVM) with linear, rbf, and sigmoid kernels; decision tree (DT); and random forest (RF) classifiers, with synthetic minority oversampling technique (SMOTE). Models underwent 100-time repeat nested cross-validation. Overall performances of our classification models were modest, and TA based on T1-weighted IP/OP/WO images provided better performance than those based on ADC and T2* maps. The most favorable result was observed in the T1-weighted WO image using RF classifier and the combination model was derived from all T1-weighted images using SVM classifier with rbf kernel. Among the selected TFs, total energy and energy had weak correlations with eGFR.

Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors.

OBJECTIVE: To evaluate the diagnostic utility of conventional magnetic resonance imaging (MRI)-based characteristics and a texture analysis (TA) for discriminating between ovarian thecoma-fibroma groups (OTFGs) and ovarian granulosa cell tumors (OGCTs). METHODS: This retrospective multicenter study enrolled 52 patients with 32 OGCTs and 21 OTFGs, which were dissected and pathologically diagnosed between January 2008 and December 2019. MRI-based features (MBFs) and texture features (TFs) were evaluated and compared between OTFGs and OGCTs. A least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select features and construct the discriminating model. ROC analyses were conducted on MBFs, TFs, and their combination to discriminate between the two diseases. RESULTS: We selected 3 features with the highest absolute value of the LASSO regression coefficient for each model: the apparent diffusion coefficient (ADC), peripheral cystic area, and contrast enhancement in the venous phase (VCE) for the MRI-based model; the 10th percentile, difference variance, and maximal correlation coefficient for the TA-based model; and ADC, VCE, and the difference variance for the combination model. The areas under the curves of the constructed models were 0.938, 0.817, and 0.941, respectively. The diagnostic performance of the MRI-based and combination models was similar (p = 0.38), but significantly better than that of the TA-based model (p < 0.05). CONCLUSIONS: The conventional MRI-based analysis has potential as a method to differentiate OTFGs from OGCTs. TA did not appear to be of any additional benefit. Further studies are needed on the use of these methods for a preoperative differential diagnosis of these two diseases.

Utility of magnetic resonance imaging for differentiating malignant mesenchymal tumors of the uterus from T2-weighted hyperintense leiomyomas.

PURPOSE: To generate a new discrimination method to distinguish between malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma based on magnetic resonance imaging findings and clinical features. MATERIALS AND METHODS: Data from 32 tumors of 32 patients with malignant mesenchymal tumors of the uterus and from 34 tumors of 30 patients with T2-weighted hyperintense leiomyoma were analyzed. Clinical parameters, qualitative magnetic resonance imaging features, including computed diffusion-weighted imaging, and quantitative characteristics of magnetic resonance imaging of these two tumor types were compared. Predictive values for malignant mesenchymal tumors of the uterus were calculated using variant discriminant analysis. RESULTS: The T1 bright area on qualitative assessment and mean apparent diffusion coefficient value on quantitative assessment yielded the most independent magnetic resonance imaging differentiators of malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma. The classification accuracy of the variant discriminant analysis based on three selected findings, i.e., a T1 bright area, computed diffusion-weighted imaging with a b-value of 2000s/mm2 (cDWI2000), and T2-hypointense bands, was 84.8% (56/66), indicating high accuracy. CONCLUSIONS: Variant discriminant analysis using the T1 bright area, cDWI2000, and T2-hypointense bands yielded high accuracy for differentiating between malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma.

Signal and morphological changes in the endolymph of patients with vestibular schwannoma on non-contrast 3D FLAIR at 3 Tesla.

BACKGROUND: Non-contrast FLAIR revealed increased signal within the inner ear in patients with vestibular schwannoma, which is generally assumed to occur in the perilymph; however, the majority of previous studies did not differentiate between the endolymph and perilymph. Therefore, endolymph signal changes have not yet been investigated in detail. The purpose of the present study was three-fold: (1) to assess perilymph signal changes in patients with vestibular schwannoma on heavily T2-weighted (T2W) 3D FLAIR, also termed positive perilymphatic images (PPI), (2) to evaluate signal and morphological changes in the endolymph on PPI, and (3) to establish whether vertigo correlates with the signal intensity ratios (SIR) of the vestibular perilymph or vestibular endolymphatic hydrops. METHODS: Forty-two patients with unilateral vestibular schwannoma were retrospectively recruited. We semi-quantitatively and qualitatively evaluated the perilymph signal intensity on the affected and unaffected sides. We also quantitatively examined the signal intensity of the vestibular perilymph and assessed the relationship between vertigo and the SIR of the vestibular perilymph on the affected side. We semi-quantitatively or qualitatively evaluated the endolymph, and investigated whether vestibular hydrops correlated with vertigo. RESULTS: The perilymph on the affected side showed abnormal signal more frequently (signal intensity grade: overall mean 1.45 vs. 0.02; comparison of signal intensity: overall mean 36 vs. 0 cases) and in more parts (the entire inner ear vs. the basal turn of the cochlea and vestibule) than that on the unaffected side. No significant difference was observed in the SIR of the vestibular perilymph with and without vertigo (5.54 vs. 5.51, p = 0.18). The endolymph of the vestibule and semicircular canals showed the following characteristic features: no visualization (n = 4), signal change (n = 1), or vestibular hydrops (n = 10). A correlation was not observed between vestibular hydrops and vertigo (p = 1.000). CONCLUSIONS: PPI may provide useful information on signal and morphological changes in the endolymph of patients with vestibular schwannoma. Further research is warranted to clarify the relationship between vertigo and the MR features of the inner ear.

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.

To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic dermatomyositis (ADM), 19 with polymyositis (PM) and 19 with non-IIM were enrolled. Using 2D manual segmentation, 93 original features as well as 93 local binary pattern (LBP) features were extracted from MRI (short-tau inversion recovery [STIR] imaging) of proximal limb muscles. To construct and compare ML models that predict disease groups using each set of features, dimensional reductions were performed using a reproducibility analysis by inter-reader and intra-reader correlation coefficients, collinearity analysis, and the sequential feature selection (SFS) algorithm. Models were created using the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF) and multi-layer perceptron (MLP) classifiers, and validated using tenfold cross-validation repeated 100 times. We also investigated whether it was possible to construct models predicting autoantibody status. Our ML-based MRI radiomics models showed the potential to distinguish between PM, DM, and ADM. Models using LBP features provided better results, with macro-average AUC values of 0.767 and 0.714, accuracy of 61.2 and 61.4%, and macro-average recall of 61.9 and 59.8%, in the LDA and k-NN classifiers, respectively. In contrast, the accuracies of radiomics models distinguishing between non-IIM and IIM disease groups were low. A subgroup analysis showed that classification models for anti-Jo-1 and anti-ARS antibodies provided AUC values of 0.646-0.853 and 0.692-0.792, with accuracy of 71.5-81.0 and 65.8-78.3%, respectively. ML-based TA of muscle MRI may be used to predict disease groups or the autoantibody status in patients with IIM and is useful in non-invasive assessments of disease mechanisms.

The product of trunk muscle area and density on the CT image is a good indicator of energy expenditure in patients with or at risk for COPD.

BACKGROUND: Physical inactivity due to cachexia and muscle wasting is well recognized as a sign of poor prognosis in chronic obstructive pulmonary disease (COPD). However, there have been no reports on the relationship between trunk muscle measurements and energy expenditure parameters, such as the total energy expenditure (TEE) and physical activity level (PAL), in COPD. In this study, we investigated the associations of computed tomography (CT)-derived muscle area and density measurements with clinical parameters, including TEE and PAL, in patients with or at risk for COPD, and examined whether these muscle measurements serve as an indicator of TEE and PAL. METHODS: The study population consisted of 36 male patients with (n = 28, stage 1-4) and at risk for (n = 8) COPD aged over 50 years. TEE was measured by the doubly labeled water method, and PAL was calculated as the TEE/basal metabolic rate estimated by the indirect method. The cross-sectional areas and densities of the pectoralis muscles, rectus abdominis muscles, and erector spinae muscles were measured. We evaluated the relationship between these muscle measurements and clinical outcomes, including body composition, lung function, muscle strength, TEE, and PAL. RESULTS: All the muscle areas were significantly associated with TEE, severity of emphysema, and body composition indices such as body mass index, fat-free mass, and trunk muscle mass. All trunk muscle densities were correlated with PAL. The product of the rectus abdominis muscle area and density showed the highest association with TEE (r = 0.732) and PAL (r = 0.578). Several trunk muscle measurements showed significant correlations with maximal inspiratory and expiratory pressures, indicating their roles in respiration. CONCLUSIONS: CT-derived measurements for trunk muscles are helpful in evaluating physical status and function in patients with or at risk for COPD. Particularly, trunk muscle evaluation may be a useful marker reflecting TEE and PAL.

An Unusual Ovarian Mucinous Borderline Tumor with a Large Solid Component.

Herein, we report magnetic resonance imaging (MRI) findings of a mucinous borderline tumor of the ovary, which we observed as a mainly solid tumor with large solid components in the lower pelvic cavity. The appearance of ovarian epithelial tumors on imaging is often complex. Cystic to solid appearing masses may be observed, and they often resemble epithelial carcinoma. Due to mucinous or hemorrhage components of packed small or microcystic components, MRI depicts slightly high signal intensity on T1-weighted images and low signal intensity on T2-weighted images. Mucinous borderline tumor of the ovary with a large solid component is very rare, but it is clinically important to recognize the possibility of mucinous borderline tumor to avoid unnecessary surgical intervention.

Diffusion-weighted MR imaging findings of ovarian adenocarcinofibromas and adenofibromas.

We retrospectively evaluated pelvic magnetic resonance imaging including diffusion-weighted imaging (DWI) of 16 ovarian lesions (5 adenocarcinofibromas, 2 borderline adenofibromas, and 9 benign adenofibromas). All adenocarcinofibromas were detected as large solid areas of strong high signal on DWI, and seven of nine benign adenofibromas and both borderline adenofibromas demonstrated no areas of high signal or small areas of weak high signal. Solid components that appear as areas of strong high signal on DWI might represent a characteristic finding of adenocarcinofibromas.

Unusual MR findings in ovarian leiomyoma.

We report a rare case of ovarian leiomyoma with extensive edema in a 55-year-old woman. Magnetic resonance (MR) imaging revealed an ovarian mass with distinct portions of predominantly low intensity and predominantly high intensity on T2-weighted image. The portion with low signal showed weak enhancement on contrast study, and the portion with high intensity suggested extensive edema of fibrous stroma. Furthermore, the mass showed low signal intensity similar to that of the myometrium on diffusion-weighted image and yielded high values on apparent diffusion coefficient (ADC) map image. Extensive edema of ovarian leiomyoma shows unusual MR imaging findings that require careful interpretation.

(1)H MR spectroscopy with external reference solution at 1.5 T for differentiating malignant and benign breast lesions: comparison using qualitative and quantitative approaches.

OBJECTIVE: To compare the diagnostic capability of proton ((1)H) magnetic resonance spectroscopy (MRS) in differentiating benign from malignant breast lesions on the basis of qualitative and quantitative approaches. METHODS: We performed single-voxel (1)H MRS for 208 breast lesions, identified a clear total composite choline compounds (tCho) peak of signal-to-noise of ≥2 to represent malignancy (qualitative approach), and regarded tCho concentration equal to or greater than the cut-off value to represent malignancy (quantitative approach). We compared the diagnostic ability of both approaches using the Akaike information criterion (AIC) and McFadden's R (2). RESULTS: Histologically, 169 lesions were malignant; 39 were benign. The qualitative approach demonstrated 84.6 % sensitivity and 51.3 % specificity for differentiating malignant and benign lesions. The mean tCho concentration was 1.13 mmol/kg for malignancy, 0.43 mmol/kg for benignity. The optimal cut-off point was 0.61 mmol/kg, use of which achieved 68.1 % sensitivity and 79.4 % specificity. Calculated AIC and R (2) score suggested the superiority of the quantitative approach for differentiating malignancy. CONCLUSIONS: Quantitative MRS provides higher specificity than qualitative MRS for differentiating malignant from benign lesions and could be more useful as an additional examination in routine breast MR imaging.

MR imaging of polypoid endometriosis of the ovary.

We report a case of polypoid endometriosis of the ovary and correlate magnetic resonance (MR) and pathological findings. The endometriosis appeared as multiple polypoid areas along the wall of the ovarian cystic lesion with hyperintensity on T2-weighted images and slight hyperintensity on diffusion-weighted images. However, the polypoid areas did not yield low apparent diffusion coefficient (ADC) values on ADC map images. These MR findings were similar to the signal intensity of the uterine endometrium, reflecting the presence of abundant endometrial glands.

Vertebral artery variations at the C1-2 level diagnosed by magnetic resonance angiography.

INTRODUCTION: The craniovertebral junction is clinically important. The vertebral artery (VA) in its several variations runs within this area. We report the prevalence of these VA variations on magnetic resonance angiography (MRA). METHODS: We retrospectively reviewed MRA images, obtained using two 1.5-T imagers, of 2,739 patients, and paid special attention to the course and branching of the VA at the level of the C1-2 vertebral bodies. RESULTS: There were three types of VA variation at the C1-2 level: (1) persistent first intersegmental artery (FIA), (2) VA fenestration, and (3) posterior inferior cerebellar artery (PICA) originating from the C1/2 level. The overall prevalence of these three variations was 5.0%. There was no laterality in frequency, but we found female predominance (P < 0.05). We most frequently observed the persistent FIA (3.2%), which was sometimes bilateral. We found VA fenestration (0.9%) and PICA of C1/2 origin (1.1%) with almost equal frequency. Two PICAs of C1/2 origin had no normal VA branch. CONCLUSIONS: We frequently observed VA variations at the C1-2 level and with female predominance. The persistent FIA was most prevalent and sometimes seen bilaterally. Preoperative identification of these variations in VA is necessary to avoid complications during surgery at the craniovertebral junction.

Usefulness of diffusion-weighted imaging of breast tumors: quantitative and visual assessment.

PURPOSE: We evaluated the usefulness of quantitative and visual assessment of diffusion-weighted imaging (DWI) of breast tumors to distinguish malignant from benign tumors. MATERIALS AND METHODS: The DWI findings of 106 breast lesions (15 benign, 91 malignant) were retrospectively analyzed. The mean apparent diffusion coefficient (ADC) value for each lesion was calculated using b values of 250, 500, 750, and 1000 s/mm(2) as a quantitative assessment. We visually evaluated the signal intensity of each breast lesion on the basis of a spinal signal intensity in DWI (b = 1000 s/mm(2)) and compared the mean ADC values using a threshold mean ADC +1.65 × standard deviation (SD) for malignant and benign breast lesions. Obviously strong signal intensity of the lesion relative to that of the spinal cord on DWI signifies malignancy. RESULTS: The mean ADC value for benign lesions (1.50 ± 0.38 × 10(-3) mm(2)/s) was significantly higher than that for malignant lesions (0.98 ± 0.19 × 10(-3) mm(2)/s), with 94.5% sensitivity, 80% specificity, and 92.5% accuracy. Sensitivity for visual assessment was 91.5%, specificity was 33.3%, and total accuracy was 82.5%. CONCLUSION: ADC values, but not visual assessment, may be useful for differentiating benign and malignant breast tumors.

Size of noncancerous hilomediastinal lymph nodes measured on coronal and sagittal reconstruction CT images.

PURPOSE: To determine the sizes of hilomediastinal lymph nodes on coronal and sagittal reconstruction computed tomographic images of subjects without known malignancies. MATERIALS AND METHODS: We evaluated 560 lymph nodes of 246 consecutive patients who underwent multidetector-row computed tomography (MDCT) of the chest, then reconstructed coronal and sagittal images on a viewer and measured short-axis diameters of lymph nodes in each station according to the American Thoracic Society (ATS) map for axial, coronal, and sagittal images. RESULTS: On coronal images, short-axis diameters were significantly larger than on axial images in station #4R (P < 0.01). On sagittal images, short-axis diameters were significantly smaller than on axial images in stations #4L (P < 0.01), #10R (P < 0.001), and #10L (P < 0.05). On coronal and sagittal images, short-axis diameters were significantly smaller than on axial images in stations #11R (P < 0.001). In #7, diameters were significantly larger on coronal images than on axial and sagittal images (P < 0.001), and diameters were significantly smaller on sagittal images than on axial images (P < 0.01). CONCLUSION: In stations #4R, #4L, #7, #10R, #10L, and #11R, measurements of short-axis diameters of hilomediastinal lymph nodes differed on coronal and sagittal images. On coronal and sagittal images, evaluation of hilomediastinal lymph nodes requires unique size criteria for every station.

Computed tomographic air-contrast enema imaging for presurgical examination of colon tumors: assessment with colon phantoms and in patients.

PURPOSE: We investigated the mechanism of colon wall deformities in phantoms and assessed the relation between pathological T staging and wall deformity in patients using computed tomography (CT) enema imaging. MATERIALS AND METHODS: We performed multidetector-row CT in mass-containing phantoms with no structural deformities and in 36 patients with colon tumors. In the phantoms, we compared images on double-contrast barium (DCB) and CT enema studies, and we compared wall deformities on CT enema studies and the actual phantom structure. In patients, we compared wall deformities on CT enema studies and T staging. RESULTS: Images from CT enema and DCB studies were identical in the phantom, and all profile images showed geometrical basal indentations. In patients, the grade of deformity increased with invasion depth; and the diagnostic accuracy of T staging was 82.5%. Geometrical basal indentation and poor colon expansion were the primary reasons for overestimating T staging on CT enema imaging. CONCLUSION: Although CT enema imaging allows reasonable diagnostic accuracy of T staging, wall deformity is thought to be consistent with tumor infiltration and other factors. We recommend evaluating T staging using not only CT enema imaging but also other techniques, such as virtual endoscopy, axial imaging, and multiplanar reformation imaging.

Situs inversus totalis with malignant lymphoma of the stomach: report of a case.

Situs inversus totalis is a rare congenital anomaly that often occurs concomitantly with other disorders. We report a case of situs inversus totalis with malignant lymphoma of the stomach, which was successfully treated by surgery followed by chemotherapy and irradiation. The patient was a 51-year-old woman who present with colicky pain in the left upper quadrant of her abdomen. Chest X-ray showed a right-sided heart, and ultrasonography and computed tomography (CT) of the abdomen showed a situs inversus totalis with multiple gallstones in the gallbladder. Tree-dimensional reconstructed CT of the abdomen showed no other malformations coexisting with situs inversus totalis, but a barium upper gastrointestinal series found an inverted stomach and an elevated tumor with ulceration in the center, localized in the antrum of the stomach. First, we performed a cholecystectomy, followed by a total gastrectomy with dissection of the lymph nodes and splenectomy, and Roux-en-Y reconstruction. Histopathological examination confirmed a diagnosis of malignant lymphoma of the stomach (diffuse large B-cell type) with metastasis to the regional lymph nodes. Chemotherapy using the CHOP regimen was given three times, starting 1 month postoperatively. A followup CT scan showed enlargement of one lymph node around the abdominal aorta and irradiation was delivered to the area of the inverted Y in the abdomen. At the time of writing, 10 months after surgery, the patient is well with no signs of recurrence and leading a normal life. Careful preoperative assessment is very important for determining the most appropriate surgical procedure in patients with situs inversus totalis associated with a malignancy.