OBJECTIVE: Recent investigations have revealed DNA mismatch repair (MMR) gene mutations are closely related with carcinogenesis of endometrial cancer; however the impact of MMR protein expression on prognosis is not determined. Correlations between MMR-related protein expression and clinicopathological factors of endometrial cancers are analyzed in the present study. METHODS: A total of 191 endometrial cancer tissues treated between 1990 and 2007 in our hospital were enrolled. Immunoreactions for MSH2, MLH1, MSH6, and PMS2 on tissue microarray specimens and clinicopathological features were analyzed retrospectively. RESULTS: Seventy-six cases (40%) had at least one immunohistochemical alteration in MMR proteins (MMR-deficient group). There were statistically significant differences of histology, International Federation of Gynecology and Obstetrics (FIGO) stage, and histological grade between MMR-deficient group and the other cases (MMR-retained group). Response rate of first-line chemotherapy in evaluable cases was slightly higher in MMR-deficient cases (67% vs. 44%, p=0.34). MMR-deficient cases had significantly better progression-free and overall survival (OS) compared with MMR-retained cases. Multivariate analysis revealed MMR status was an independent prognostic factor for OS in endometrial cancers. CONCLUSION: MMR-related proteins expression was identified as an independent prognostic factor for OS, suggesting that MMR was a key biomarker for further investigations of endometrial cancers.
Adaptor Proteins, Signal Transducing/deficiency/metabolism ; Adenosine Triphosphatases/deficiency/metabolism ; Adult ; Aged ; Aged, 80 and over ; Chemotherapy, Adjuvant ; *DNA Mismatch Repair ; DNA Repair Enzymes/deficiency/*metabolism ; DNA-Binding Proteins/deficiency/*metabolism ; Endometrial Neoplasms/*diagnosis/drug therapy/genetics/pathology ; Female ; Humans ; Kaplan-Meier Estimate ; Middle Aged ; MutS Homolog 2 Protein/deficiency/metabolism ; Neoplasm Proteins/deficiency/metabolism ; Nuclear Proteins/deficiency/metabolism ; Prognosis ; Retrospective Studies ; Tumor Markers, Biological/*metabolism

OBJECTIVE: Compared with serous adenocarcinoma (SAC), clear cell carcinoma (CCC) often shows chemo-resistance, which would potentially lead to a poor prognosis. On the other hand, there have been arguments over prognoses of CCC and SAC disease. In the present study, multivariate analysis to compare prognosis of CCC patients with that of SAC was aimed for the patients selected from central pathologic review. METHODS: Between 1984 and 2009, a total of 500 ovarian cancer patients were treated at our university hospital. Among them, 111 patients with CCC and 199 patients with SAC were identified through central pathological review. Overall survival and progression-free survival were compared using Kaplan-Meier method, and prognostic factors were investigated by multiple regression analyses. RESULTS: Median age was 52 years for CCC and 55 years for SAC (p=0.03). The ratio of stage I patients were significantly higher in CCC compared with SAC (55% vs. 13%, p<0.01). Among evaluable cases, response rate was significantly lower in CCC than that in SAC (32% vs. 78%, p<0.01). No significant differences of progression-free survival and overall survival were observed in stage I patients; however, prognoses of CCC were significantly poorer than those of SAC in advanced-stage disease. In stage II-IV patients, not only residual tumors and clinical stages, but also clear cell histology were identified as predictors for poor prognosis. CONCLUSION: Clear cell histology was identified as a prognostic factor for advanced-stage ovarian cancers. Histologic subtypes should be considered in further clinical studies, especially for advanced epithelial ovarian cancers.
Adenocarcinoma ; Adenocarcinoma, Clear Cell ; Chlormequat ; Cystadenocarcinoma, Serous ; Disease-Free Survival ; Hand ; Humans ; Multivariate Analysis ; Neoplasm, Residual ; Ovarian Neoplasms ; Prognosis

Objective: To apply the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system to all patients who underwent trachelectomy in our previous study and to update the oncologic and obstetric results. Methods: We retrospectively reviewed the medical records of patients in whom abdominal trachelectomy was attempted between June 2005 and September 2021. The FIGO 2018 staging system for cervical cancer was applied to all patients. Results: Abdominal trachelectomy was attempted for 265 patients. Trachelectomy was converted to hysterectomy in 35 patients, and trachelectomy was completed successfully in 230 (conversion rate: 13%). Applying the FIGO 2018 staging system, 40% of the patients who underwent radical trachelectomy had stage IA tumors. Among 71 patients who had tumors measuring ≥2 cm, 8 patients were classified as stage IA1 and 14 as stage IA2. Overall recurrence and mortality rates were 2.2% and 1.3%, respectively. One hundred twelve patients attempted to conceive after trachelectomy; 69 pregnancies were achieved in 46 patients (pregnancy rate: 41%). Twenty-three pregnancies ended in first-trimester miscarriage, and 41 infants were delivered between gestational weeks 23 and 37; 16 were deliveries at term (39%) and 25 were premature deliveries (61%). Conclusion This study suggested that patients judged to be ineligible for trachelectomy and patients receiving overtreatment will continue to appear using the current standard eligibility criteria. With the revisions to the FIGO 2018 staging system, the preoperative eligibility criteria for trachelectomy, which were based on the FIGO 2009 staging system and tumor size, should be changed.

Objective: For oncologic safety, vaginal radical trachelectomy is generally performed only in patients with cervical cancers smaller than 2 cm. However, because inclusion criteria for abdominal trachelectomy are controversial, we evaluated the safety of abdominal trachelectomy for cervical cancers ≥2 cm. Methods: We began performing abdominal trachelectomies at our institution in 2005, primarily for squamous cell carcinoma ≤3 cm or adenocarcinoma/adenosquamous carcinoma ≤2 cm. If a positive sentinel lymph node or cervical margin was diagnosed intraoperatively by frozen section, the trachelectomy was converted to a hysterectomy. Medical records of these patients were reviewed retrospectively. Patients who had undergone simple abdominal trachelectomy were excluded from this study. Results: We attempted trachelectomy in 212 patients. Among the 135 patients with tumors <2 cm, trachelectomy was successful in 120, one of whom developed recurrence and none of whom died of their disease. Among 77 patients with tumors ≥2 cm, trachelectomy was successful in 62, 2 of whom developed recurrence and 1 of whom died of her disease. The overall relapse rate after trachelectomy was 1.6% (0.8% in <2 cm group and 3.2% in ≥2 cm group), and the mortality rate was 0.5% (0% in <2 cm group and 1.6% in ≥2 cm group).Recurrence-free survival (p=0.303) and overall survival (p=0.193) did not differ significantly between the <2 cm and ≥2 cm groups. Conclusions Abdominal trachelectomy with intraoperative frozen sections of sentinel lymph nodes and cervical margins is oncologically safe, even in patients with tumors ≥2 cm.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

PURPOSE: The aim of this study was to evaluate the validity and reliability of the Seattle Angina Questionnaire, Japanese version (SAQ-J) as a disease-specific health outcome scale in patients with coronary artery disease. METHODS: Patients with coronary artery disease were recruited from a university hospital in Tokyo. The patients completed self-administered questionnaires, and medical information was obtained from the subjects' medical records. Face validity, concurrent validity evaluated using Short Form 36 (SF-36), known group differences, internal consistency, and test-retest reliability were statistically analyzed. RESULTS: A total of 354 patients gave informed consent, and 331 of them responded (93.5%). The concurrent validity was mostly supported by the pattern of association between SAQ-J and SF-36. The patients without chest symptoms showed significantly higher SAQ-J scores than did the patients with chest symptoms in 4 domains. Cronbach's alpha ranged from .51 to .96, meaning that internal consistency was confirmed to a certain extent. The intraclass correlation coefficient of most domains was higher than the recommended value of 0.70. The weighted kappa ranged from .24 to .57, and it was greater than .4 for 14 of the 19 items. CONCLUSIONS: The SAQ-J could be a valid and reliable disease-specific scale in some part for measuring health outcomes in patients with coronary artery disease, and requires cautious use.
Asian Continental Ancestry Group ; Coronary Artery Disease ; Coronary Vessels ; Humans ; Informed Consent ; Medical Records ; Reproducibility of Results ; Thorax ; Tokyo ; Surveys and Questionnaires

BACKGROUND/AIMS: In clinical practice, colonoscopy has been regarded as the gold standard for the evaluation of disease severity as well as mucosal healing in ulcerative colitis (UC). Some activity indices incorporating patient symptoms as parameters have been shown to reflect the endoscopic activity of UC. The aim of this study was to examine whether self-reported symptoms with visual analog scales (VAS) can predict endoscopic activity. METHODS: A cross-sectional study of 150 UC patients who underwent colonoscopy with submission of VAS scores of 4 symptoms: general condition, bloody stools, stool form, and abdominal pain (0: no symptoms, 10: the most severe symptoms). Each VAS score was compared with colonoscopic activity assessed with the Mayo endoscopic subscore (MES). RESULTS: All VAS scores were significantly correlated with the endoscopic severity (Spearman correlation coefficients of general condition, bloody stools, stool form, and abdominal pain: 0.63, 0.64, 0.58, and 0.43, respectively). Mucosal healing defined as MES 0 alone was predicted by VAS score < 1.5 on general condition or 0 on bloody stools with sensitivity of 0.84 and 0.76 and specificity of 0.66 and 0.76, respectively. Additionally, VAS score < 2.5 on stool form predicted active lesions in distal colorectum alone with sensitivity of 0.67 and specificity of 0.66, suggesting that this item could predict the indication of topical therapy. CONCLUSIONS: Self-reported VAS scores on symptoms were correlated with endoscopic activity of UC. To clarify the relationship between VAS and mucosal healing, further validation studies are needed.
Abdominal Pain ; Colitis, Ulcerative* ; Colonoscopy ; Cross-Sectional Studies ; Humans ; Sensitivity and Specificity ; Ulcer* ; Visual Analog Scale*

Objective: To implement early rehabilitation interventions by physical therapists is recommended. However, the effectiveness of early rehabilitation for severe coronavirus disease 2019 (COVID-19) patients in the prevention of post-intensive care syndrome (PICS) is unclear. We analyzed a multicenter prospective observational study (Post-Intensive Care outcomeS in patients with COronaVIrus Disease 2019) to examine the association between early rehabilitation interventions and PICS physical impairment. Methods: An analysis was performed on COVID-19 patients who were admitted to intensive care units (ICUs) between March 2020 and March 2021, and required mechanical ventilation. The primary outcome was the incidence of PICS physical impairment (Barthel Index≤90) after one year. Multivariate logistic regression analysis was used to estimate the association between early rehabilitation interventions and PICS physical impairment by adjusting ICU mobility scale (IMS) during seven-day following ICU admission, and clinically relevant risk factors. Results: The analysis included 259 patients, 54 of whom developed PICS physical impairment one year later. In 81 patients, physical therapists intervened within seven days of ICU admission. There was no significant difference in mean IMS by day seven of admission between the early and non-early rehabilitation patients (0.70 and 0.61, respectively). Multivariate logistic regression analysis showed that early rehabilitation interventions were significantly associated with a low incidence of PICS physical impairment (odds ratio, 0.294; 95% confidence interval, 0.123–0.706; p=0.006). Conclusion Early rehabilitation interventions by physical therapists were an independent factor associated with the decreased development of PICS physical impairment at one year, even though early rehabilitation had no significant effect on IMS.