Quantitative ELISA-Like Immunohistochemistry of Fibroblast Growth Factor 23 in Diagnosis of Tumor-Induced Osteomalacia and Clinical Characteristics of the Disease

Tumor-induced osteomalacia (TIO) is a rare acquired paraneoplastic disorder and fibroblast growth factor 23 (FGF23) plays a key role in its pathogenesis. This study was conducted to describe a novel FGF23 detecting procedure and describe clinical features of the disease. Fourteen TIO cases were retrieved and FGF23 expression was measured by quantitative ELISA-like immunohistochemistry using formalin-fixed and paraffin-embedded tissues. As summarized from 14 TIO cases, clinical features of TIO were long-standing history of osteomalacia, hypophosphatemia, and urinary phosphate wasting. The associated tumors were mostly benign phosphaturic mesenchymal tumors mixed connective tissue variant (PMTMCT) which could be located anywhere on the body, and most of them could be localized by conventional examinations and octreotide scanning. By quantitative ELISA-like immunohistochemistry, all the 14 TIO cases had high FGF23 expression (median 0.69, 25%–75% interquartile 0.57–1.10, compared with 26 non-TIO tumors of median 0.07, 25%–75% interquartile 0.05–0.11, p<0.001). The quantitative ELISA-like immunohistochemistry was a feasible and reproducible procedure to detect the high FGF23 expression in formalin-fixed and paraffin-embedded biopsies or specimens. Since TIO was often delay-diagnosed or misdiagnosed, clinicians and pathologists should be aware of TIO and PMTMCT, respectively

Nab-paclitaxel plus S-1 in advanced pancreatic adenocarcinoma (NPSPAC): a single arm, single center, phase II trial

This single-arm, phase II trial is to investigate efficacy and safety of nab-paclitaxel plus S-1 as first-line treatment in advanced pancreatic cancer. Nab-paclitaxel was administered at 120 mg/m2 intravenously on day 1 and 8, S-1 was given twice a day orally on day 1-14 of each 21-day cycle, for 6 cycles. The primary endpoint was objective response rate (ORR), the secondary endpoints were progression-free survival (PFS), overall survival (OS) and safety. The ORR in intent-to-treat population (N=60) by either blinded independent review (BIR) or investigator assessment was 50.0%. Median PFS (mPFS) by BIR and median OS (mOS) were 5.6 months (95%CI, 4.6 to 6.6 m) and 9.4 months (95%CI, 8.0 to 10.8m), respectively. The most common grade 3 or 4 toxicities were leukopenia/neutropenia (35%) and fatigue (8.3%). Subgroup analyses based on BIR showed a remarkable ORR (>70%) was achieved in patients with female gender, ≥ 50% decline from baseline CA19-9, and developed grade 3 or 4 leukopenia/neutropenia. Remarkable survival benefit was statistically significant in female (mPFS: 7.7m, mOS: 18.2m), ≥ 50% decline from baseline CA19-9 (mPFS: 6.8m, mOS: 11.8m), objective responders (mPFS: 6.9m, mOS: 12.2m), and ECOG of 0 at baseline (mPFS: 7.5m, mOS: 16.1m). Nab-paclitaxel plus S-1 showed encouraging ORR and manageable toxicities, which is an effective alternative treatment regimen for advanced pancreatic cancer. (https://clinicaltrials.gov/ number, NCT02124317

Comprehensive analysis of age‐related somatic mutation profiles in Chinese young lung adenocarcinoma patients

Abstract Background Lung adenocarcinoma in young adults is a rare entity with the oncogenic genetic alterations associated being poorly understood. In the present study, the effect of genetic alterations in lung adenocarcinoma patients diagnosed in young patients is reported. Methods Twenty young lung adenocarcinoma patients (age years: median: 33.5, range: 24‐36) were enrolled in the current study and 24 patients who were at common age of the disease onset (age years: median: 61.5, range: 52‐79) were selected for comparison. Paraffin sections of lung adenocarcinoma were analyzed using the whole‐exome sequencing platform. Results Similar number of somatic mutations per tumor were found in the young patients and their older counterparts. Although no age‐related differences were detected in the numbers of lung adenocarcinoma patients harboring well‐known gene variants, mutations in FRG1 and KMT2C were associated with a younger age especially after correcting for tobacco smoking and sex (FRG1: P = 0.027, KMT2C: P = 0.046). Five genetic variants showed higher alteration frequencies in young patients compared to the unclassified East Asian population, suggesting these mutations as disease‐related hereditary germline variants. Conclusions These results suggest different characteristics of lung adenocarcinoma between the young and the patients at common age of onset. Young patients with lung adenocarcinoma have a distinctly unique prevalence of oncogenic genetic alterations

Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists

Objectives The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study.Design The deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. We built the model based on DeepLab v2 with ResNet-34. The model performance was tested on 194 test slides and compared with five pathologists. Furthermore, the generalisation ability of the learning model was tested by extra 168 slides (111 with adenoma) collected from two other hospitals.Results The deep learning model achieved an area under the curve of 0.92 and obtained a slide-level accuracy of over 90% on slides from two other hospitals. The performance was on par with the performance of experienced pathologists, exceeding the average pathologist. By investigating the feature maps and cases misdiagnosed by the model, we found the concordance of thinking process in diagnosis between the deep learning model and pathologists.Conclusions The deep learning model for colorectal adenoma diagnosis is quite similar to pathologists. It is on-par with pathologists’ performance, makes similar mistakes and learns rational reasoning logics. Meanwhile, it obtains high accuracy on slides collected from different hospitals with significant staining configuration variations

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning

The early detection and accurate histopathological diagnosis of gastric cancer are essential factors that can help increase the chances of successful treatment. Here, the authors report on a digital pathology tool achieving high performance on a real world test dataset and show that the system can aid pathologists in improving diagnostic accuracy