Nottingham prognostic x (NPx): a risk stratification tool in ER-positive HER2-negative breast cancer: a validation study.

AIMS: In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC. MATERIALS AND METHODS: Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC. RESULTS: In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx. CONCLUSION: NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images.

Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions. This paper proposes an uncertainty-aware Deep Kernel Learning (DKL) model to detect blurry areas and folded tissues, two types of artifacts that can appear in WSIs. The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates. We achieved 0.996 and 0.938 F1 scores for blur and folded tissue detection on unseen data, respectively. In extensive experiments, we validated the DKL model on unseen data from external independent cohorts with different staining and tissue types, where it outperformed DCNNs. Interestingly, the DKL model is more confident in the correct predictions and less in the wrong ones. The proposed DKL model can be integrated into the preprocessing pipeline of CPATH systems to provide reliable predictions and possibly serve as a quality control tool.

Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization.

BACKGROUND AND OBJECTIVE: Mitotic activity is a crucial biomarker for diagnosing and predicting outcomes for different types of cancers, particularly breast cancer. However, manual mitosis counting is challenging and time-consuming for pathologists, with moderate reproducibility due to biopsy slide size, low mitotic cell density, and pattern heterogeneity. In recent years, deep learning methods based on convolutional neural networks (CNNs) have been proposed to address these limitations. Nonetheless, these methods have been hampered by the available data labels, which usually consist only of the centroids of mitosis, and by the incoming noise from annotated hard negatives. As a result, complex algorithms with multiple stages are often required to refine the labels at the pixel level and reduce the number of false positives. METHODS: This article presents a novel weakly supervised approach for mitosis detection that utilizes only image-level labels on histological hematoxylin and eosin (H&E) images, avoiding the need for complex labeling scenarios. Also, an Uninformed Teacher-Student (UTS) pipeline is introduced to detect and distill hard samples by comparing weakly supervised localizations and the annotated centroids, using strong augmentations to enhance uncertainty. Additionally, an automatic proliferation score is proposed that mimicks the pathologist-annotated mitotic activity index (MAI). The proposed approach is evaluated on three publicly available datasets for mitosis detection on breast histology samples, and two datasets for mitotic activity counting in whole-slide images. RESULTS: The proposed framework achieves competitive performance with relevant prior literature in all the datasets used for evaluation without explicitly using the mitosis location information during training. This approach challenges previous methods that rely on strong mitosis location information and multiple stages to refine false positives. Furthermore, the proposed pipeline for hard-sample distillation demonstrates promising dataset-specific improvements. Concretely, when the annotation has not been thoroughly refined by multiple pathologists, the UTS model offers improvements of up to ∼4% in mitosis localization, thanks to the detection and distillation of uncertain cases. Concerning the mitosis counting task, the proposed automatic proliferation score shows a moderate positive correlation with the MAI annotated by pathologists at the biopsy level on two external datasets. CONCLUSIONS: The proposed Uninformed Teacher-Student pipeline leverages strong augmentations to distill uncertain samples and measure dissimilarities between predicted and annotated mitosis. Results demonstrate the feasibility of the weakly supervised approach and highlight its potential as an objective evaluation tool for tumor proliferation.

Whole-Exome Sequencing Reveals High Mutational Concordance between Primary and Matched Recurrent Triple-Negative Breast Cancers.

PURPOSE: Triple-negative breast cancer (TNBC) is a molecularly complex and heterogeneous breast cancer subtype with distinct biological features and clinical behavior. Although TNBC is associated with an increased risk of metastasis and recurrence, the molecular mechanisms underlying TNBC metastasis remain unclear. We performed whole-exome sequencing (WES) analysis of primary TNBC and paired recurrent tumors to investigate the genetic profile of TNBC. METHODS: Genomic DNA extracted from 35 formalin-fixed paraffin-embedded tissue samples from 26 TNBC patients was subjected to WES. Of these, 15 were primary tumors that did not have recurrence, and 11 were primary tumors that had recurrence (nine paired primary and recurrent tumors). Tumors were analyzed for single-nucleotide variants and insertions/deletions. RESULTS: The tumor mutational burden (TMB) was 7.6 variants/megabase in primary tumors that recurred (n = 9); 8.2 variants/megabase in corresponding recurrent tumors (n = 9); and 7.3 variants/megabase in primary tumors that did not recur (n = 15). MUC3A was the most frequently mutated gene in all groups. Mutations in MAP3K1 and MUC16 were more common in our dataset. No alterations in PI3KCA were detected in our dataset. CONCLUSIONS: We found similar mutational profiles between primary and paired recurrent tumors, suggesting that genomic features may be retained during local recurrence.

Is decreased tissue elasticity more important than histopathological changes in ruptures of ascending aortic aneurysms?

Background: In this study, we aimed to investigate the natural properties of ascending aortic aneurysms and to compare dilated aortic tissues of patients with ascending aortic aneurysms and the non-pathological aortic tissues of cadavers. Methods: Between January 2017 and January 2020, a total of 14 patients (12 males, 2 females; mean age: 66.6±8.4 years; range, 54 to 77 years) who underwent elective ascending aortic surgery without genetic disease or family history in the etiology were included in the study. Aortic tissues taken from the patients with ascending aortic aneurysms and cadavers without any aortic pathology were compared histopathologically and biomechanically. An experienced pathologist performed a histological evaluation with appropriate staining and scoring. In the biomechanical examination, stress-strain curves were created with the uniaxial tensile test. The instantaneous elastic modulus was calculated based on the first regions of the curves (Ei) and the slopes of the linear region close to rupturing (Es). Results: In the pathological examination, there was no statistically significant difference in the parameters of both the patient and control groups (p>0.05). In the biomechanical examination, Ei values were significantly higher in the patients with ascending aortic aneurysms, while Es values were comparable between the groups (p=0.028 and p=0.609, respectively). Conclusion: Our study results showed that the tissues of the ascending aortic aneurysm were much more rigid, although no significant histopathological changes were detected. These findings are meaningful in understanding the structure of normal and pathological aortic tissue.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning.

Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. Methods: Serial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67 + , and pH3 + cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. Results: Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67 + , and pH3 + features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. Conclusions: Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. METHODS: Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. RESULTS: Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. CONCLUSIONS: Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge-based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge-derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions. RESULTS: The experimental dataset consists of 26 419 pathology image patches of 1000×1000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making. AVAILABILITY AND IMPLEMENTATION: The codes, the documentation and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct Gene Expression Profiles of Matched Primary and Metastatic Triple-Negative Breast Cancers.

Background: Although triple-negative breast cancer (TNBC) is associated with an increased risk of recurrence and metastasis, the molecular mechanisms underlying metastasis in TNBC remain unknown. To identify transcriptional changes and genes regulating metastatic progression in TNBC, we compared the transcriptomic profiles of primary and matched metastatic tumors using massively parallel RNA sequencing. Methods: We performed gene expression profiling using formalin-fixed paraffin-embedded (FFPE) TNBC tissues of patients from two cohorts: the Zurich cohort (n = 31) and the Stavanger cohort (n = 5). Among the 31 patients in the Zurich cohort, 18 had primary TNBC tumors that did not metastasize, and 13 had primary tumors that metastasized (11 paired primary and locoregional recurrences). The Stavanger cohort included five matched primary and metastatic TNBC tumors. Significantly differentially expressed genes (DEGs; absolute fold change ≥2, p < 0.05) were identified and subjected to functional analyses. We investigated if there was any overlap between DEGs from both the cohorts with epithelial-to-mesenchymal-to-amoeboid transition (EMAT) gene signature. xCell was used to estimate relative fractions of 64 immune and stromal cell types in each RNA-seq sample. Results: In the Zurich cohort, we identified 1624 DEGs between primary TNBC tumors and matched metastatic lesions. xCell analysis revealed a significantly higher immune scores for metastatic lesions compared to paired primary tumors in the Zurich cohort. We also found significant upregulation of three MammaPrint signature genes (HRASLS, TGFB3 and RASSF7) in primary tumors that metastasized compared to primary tumors that remained metastasis-free. In the Stavanger cohort, we identified 818 DEGs between primary tumors and matched metastatic lesions. No significant differences in xCell immune scores were observed. We found that 21 and 14 DEGs from Zurich and Stavanger cohort, respectively, overlapped with the EMAT gene signature. In both cohorts, genes belonging to the MMP, FGF, and PDGFR families were upregulated in primary tumors compared to matched metastatic lesions. Conclusions: Our results suggest that distinct gene expression patterns exist between primary TNBCs and matched metastatic tumors. Further studies are warranted to explore whether these discrete expression profiles underlie or result from disease status.

Radiological, Clinical, and Histological Findings in the Treatment of Pilonidal Sinus with Phenol Injection.

Objective: Sacrococcygeal pilonidal disease is a chronic discharging wound that causes pain and loss of quality of life. Phenol application is an outpatient procedure with low complications and low recurrence rates. We evaluated the radiological, histological, and clinical results of phenol application. Methods: A total of 44 consecutive patients with sacrococcygeal pilonidal disease underwent phenol application in Kocaeli University Faculty of Medicine, General Surgery Clinic between December 2015 and March 2017. Demographics, complaints, symptom duration, and the number of sinuses were recorded. Patients were examined using ultrasonography (USG) before surgery and two months after surgery. Four patients who wanted rhomboid excision and Limberg flap procedure underwent preoperative local phenol application and were excluded from the study. The excised tissues were used for pathological evaluation. Results: Purulent discharge was the leading symptom. All of the patients had hair in the preoperative USG evaluation, whereas five patients had still hairs in the sinuses in the postoperative USG examination. Four of these cases had recurrences on a 2-month follow-up. After phenol application, a 10% volume decrease was observed. Pathological examination was performed in four cases with phenol application, and in pathological material, the sinuses looked sclerotic and fibrotic. Conclusions: Our study revealed that a single phenol application achieved 90% success. However, no significant difference was observed in the cavity volume after phenol injection.

The Value of Immunohistochemical Methods and Preoperative Magnetic Resonance Imaging Findings in Diagnosis of IDH1 Mutant Glioblastomas.

AIM: To assess the presence of isocitrate dehydrogenase (IDH) 1 mutation in glioblastomas using real-time polymerase chain reaction (RT-PCR), which is the gold standard in the diagnosis of IDH1 mutation; by immunohistochemistry (IHC), which is available in most of the pathology laboratories; and by preoperative magnetic resonance imaging, which is a non-invasive method. We also investigated the relationship between these methods and their usability in routine practice. MATERIAL AND METHODS: RT-PCR was performed to evaluate the presence of IDH1-R132H mutation on the blocks of 70 patients diagnosed with glioblastoma, and IDH1 stain was applied to the same blocks as IHC. Radiologically, preoperative magnetic resonance images of the patients were reviewed in terms of tumor size, localization, and presence of non-contrast-enhancing solid tumor component. RESULTS: Evaluation by RT-PCR revealed that 15 (21.4%) patients were IDH-mutant, whereas IHC examination revealed 13 (18.6%) and radiological evaluation revealed 11 (15.7%) patients were IDH-mutant. There was a statistically significant difference between the IDH1 mutation detected by RT-PCR and by IHC or radiological methods (p=0.034 and p=0.000, respectively). The sensitivity and specificity of IHC method in detecting IDH1 mutation were 86.6% and 100%, respectively, whereas those of radiological methods were 33.3% and 89%, respectively. CONCLUSION: Conclusively, radiological and IHC methods can be used in cases where RT-PCR cannot be applied for detecting IDH1 mutation. However, the results need to be confirmed by RT-PCR when necessary as these methods may sometimes overlook some IDH-mutant patients.

Breast Recurrence of Acute Myeloid Leukemia After Bone Marrow Transplantation: A Case Report About Myeloid Sarcoma of the Breast.

Myeloid sarcoma of the breast is a rare malignancy, can be seen after bone marrow transplantation. Although there are no specific features for this malignancy which is difficult to diagnose, some common features draw attention in the published case reports. Since there is no consensus on the treatment of myeloid sarcoma of the breast, we aimed to explain our own diagnosis and treatment methods in this case report.

Infiltration of thyroid papillary cancer tissue with myeloid leukemic cells: a case report.

BACKGROUND: Extramedullary leukemia, also known as myeloid sarcoma, is a rare form of acute myeloid leukemia and often accompanies bone marrow involvement. Myeloid infiltration of the thyroid gland is extremely rare. Here, a unique case in which thyroid cancer tissue was infiltrated with myeloid cells is presented. CASE PRESENTATION: We present a case of thyroid papillary cancer infiltrated by blastic cells and bilateral breast and axillary myeloid sarcoma in a 30-year-old Caucasian female patient with a history of osteosarcoma and MDS-RAEB2. The patient firstly received 6 cycles of chemotherapy for osteosarcoma, and allogeneic hematopoietic stem cell transplantation was performed after anthracycline-based chemotherapy due to MDS-RAEB2. The patient remained in remission on follow-up in terms of both osteosarcoma and MDS-RAEB2. Malignant features (Bethesda VI) were observed in the fine needle aspiration biopsy performed from a newly developed firm, fixed thyroid nodule approximately 4-5 cm in length in the left thyroid lobe. Because of the Bethesda VI thyroid nodule, the patient underwent total thyroidectomy. In the pathological evaluation, CD34-, CD117-, MPO-, and HLA-DR-positive blastic cells which infiltrated into follicular variant papillary thyroid carcinoma were detected. In the evaluation performed due to blastic cell infiltration, multiple lesions showing increased 18-fluorodeoxyglucose activity in bilateral breast and axillae were detected. Myeloid sarcoma was found as a result of tru-cut biopsy from these lesions. A fungal cystic lesion was detected in the frontal region of the patient who developed altered consciousness after the second cycle of treatment of myeloid sarcoma. During her follow-up in the intensive care unit, she died of cranial septic embolism and acute infarction. CONCLUSIONS: Here, we present a very interesting case that is the first. A staged approach to diagnosis with methods including immunohistochemical staining, radiological imaging methods, and cytogenetic and molecular analyses can help make the definitive diagnosis.

Solid variant of papillary thyroid carcinoma: An analysis of 28 cases with current literature.

INTRODUCTION: Solid variant papillary thyroid cancer (SVPTC) is a rare variant of papillary thyroid carcinoma (PTC) and its prognostic value is still unclear. Therefore, we re-evaluate the histopathological and clinicopathological features of 28 patients with SVPTC in the light of current literature. MATERIAL-METHODS: Of the 1308 cases were previously diagnosed with PTC and 28 (2,1%) of them which had been diagnosed with SVPTC were re-evaluated retrospectively. RESULTS: Of the 28 patients with SVPTC, 85.7% were female, mean age was 45.18 years and mean tumor diameter was 2.96 cm. Microscopically; tumors had a solid growth pattern amounting to at least 50.0% of the tumor volume. In all cases the tumor cells had characteristic nuclear features of conventional PTC. 11 patients had multifocal tumors, extrathyroidal extension was present in 4 patients and vascular invasion was observed in 7 cases. Regional lymph node metastases were noted in 2 (7.1%) cases at the time of diagnosis. One patient died because of locally advanced disease. Another patient is alive with lung metastases after 48 months from the initial surgery. There was no evidence of local recurrence in other patient. CONCLUSIONS: SVPTC is a rare variant of PTC that should be considered in the differential diagnosis of tumors which show a solid/trabecular growth pattern in the thyroid. It has poor prognostic features such as widespread angioinvasion, extrathyroidal extention, lymph node metastasis, and distant organ metastasis. Multicenter studies involving large number of cases are needed to reveal the prognostic significance of SVPTC, with standardized diagnostic criteria.

Giant mantle cell lymphoma in the soft tissue of the leg: rare presentation.

BACKGROUND: Mantle cell lymphomas are aggressive, mature B-cell neoplasms characteristically showing overexpression of cyclin D1. Although lymphadenopathy is the most common presentation, involvement of extranodal sites including bone marrow, peripheral blood, liver, gastrointestinal system, and Waldeyer ring is also seen frequently. Soft tissue localization is extremely rare. It has blastoid and pleomorphic subtypes associated with aggressive course. CASE DESCRIPTION: We describe a 74-year-old man who had been diagnosed 3 years previously with "mantle cell lymphoma-blastoid type" and presented 3 months ago with a giant mass in the right lower extremity that enlarged rapidly up to 15 cm in a few months. CONCLUSION: We present this rare presentation, which was evaluated in favor of hemangioma before biopsy, together with the data in the literature to emphasize the need for differential diagnosis, especially in cases with a clinical history.

Cushing's disease due to a pituitary adenoma as a component of collision tumor: A case report and review of the literature.

BACKGROUND: The coexistence of two morphologically different tumors attached to each other creates a very rare type of tumor called a collision tumor. Collision tumors containing pituitary adenoma-sellar meningioma have only been described in four cases to date; we discuss a fifth case harboring a collision tumor comprising a pituitary corticotroph adenoma and a sellar meningioma in the same anatomic position. CASE PRESENTATION: A 34-year-old Caucasian woman presented with menstrual irregularity, severe weakness of the proximal muscles, and 10-15 kg weight gain within a year. Basal plasma cortisol and adrenocorticotrophic hormone levels were 17.7 mg/dL and 58 pg/mL, respectively. Her diurnal cortisol rhythm was impaired (plasma cortisol at 23:00, 18.2 mg/dL) and after a 48-hour, 2-mg dexamethasone suppression test, plasma cortisol level was 13.6 mg/dL. The results were consistent with a diagnosis of Cushing's syndrome. We then performed a nocturnal 8-mg dexamethasone suppression test and the suppression of cortisol was not greater than 50% (21.4 to 19.3). A pituitary magnetic resonance imaging revealed a tuberculum sellae meningioma arising from within the sellar region. An operation was chosen in order to examine whether the tumor was an adrenocorticotrophic hormone/corticotropin-releasing hormone-secreting lesion or if there were any microadenomas that could be observed during the operation. Via an extended endoscopic endonasal approach the meningioma was resected successfully. Unexpectedly, our patient complained of nausea and vomiting postoperatively. Plasma cortisol was 2.6 mg/dL and orally administered hydrocortisone treatment was initiated immediately. Histopathological examination revealed that the tumor generally consisted of a pituitary corticotroph adenoma infiltrated by meningioma. Our patient maintained hydrocortisone treatment for 11 months. At the latest visit, she had lost 12 kg, and her hypertension, menstrual irregularity, and weakness of the proximal muscles had disappeared. Her mental and physical wellbeing were restored. CONCLUSIONS: To the best of our knowledge, this is the first report of Cushing's disease due to a pituitary corticotroph adenoma adjacent to a meningioma. Even if a high-dose dexamethasone suppression test fails to suppress basal cortisol level, the importance of considering a suprasellar/sellar meningioma a possible component of a collision tumor presenting as adrenocorticotrophic hormone-dependent Cushing's syndrome is highlighted here.