Screening programs must balance the benefit of early detection with the cost of overscreening. Here, we introduce a novel reinforcement learning-based framework for personalized screening, Tempo, and ...demonstrate its efficacy in the context of breast cancer. We trained our risk-based screening policies on a large screening mammography dataset from Massachusetts General Hospital (MGH; USA) and validated this dataset in held-out patients from MGH and external datasets from Emory University (Emory; USA), Karolinska Institute (Karolinska; Sweden) and Chang Gung Memorial Hospital (CGMH; Taiwan). Across all test sets, we find that the Tempo policy combined with an image-based artificial intelligence (AI) risk model is significantly more efficient than current regimens used in clinical practice in terms of simulated early detection per screen frequency. Moreover, we show that the same Tempo policy can be easily adapted to a wide range of possible screening preferences, allowing clinicians to select their desired trade-off between early detection and screening costs without training new policies. Finally, we demonstrate that Tempo policies based on AI-based risk models outperform Tempo policies based on less accurate clinical risk models. Altogether, our results show that pairing AI-based risk models with agile AI-designed screening policies has the potential to improve screening programs by advancing early detection while reducing overscreening.
Objective
To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting ...apparent diffusion coefficient (ADC) radiomics features.
Methods
This retrospective study involved analysis of MR images from 169 patients with cervical cancer stage IB–IVA captured; among them, diffusion-weighted (DW) images from 144 patients were used for training, and another 25 patients were recruited for testing. A U-Net convolutional network was developed to perform automated tumor segmentation. The manually delineated tumor region was used as the ground truth for comparison. Segmentation performance was assessed for various combinations of input sources for training. ADC radiomics were extracted and assessed using Pearson correlation. The reproducibility of the training was also assessed.
Results
Combining b0, b1000, and ADC images as a triple-channel input exhibited the highest learning efficacy in the training phase and had the highest accuracy in the testing dataset, with a dice coefficient of 0.82, sensitivity 0.89, and a positive predicted value 0.92. The first-order ADC radiomics parameters were significantly correlated between the manually contoured and fully automated segmentation methods (
p
< 0.05). Reproducibility between the first and second training iterations was high for the first-order radiomics parameters (intraclass correlation coefficient = 0.70–0.99).
Conclusion
U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. First-order radiomics features extracted from whole tumor volume demonstrate the potential robustness for longitudinal monitoring of tumor responses in broad clinical settings.
Summary
U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images.
Key Points
•
U-Net-based deep learning can perform accurate fully automated localization and segmentation of cervical cancer in diffusion-weighted MR images.
•
Combining b0, b1000, and apparent diffusion coefficient (ADC) images exhibited the highest accuracy in fully automated localization.
• First-order radiomics feature extraction from whole tumor volume was robust and could thus potentially be used for longitudinal monitoring of treatment responses.
A metabolomics-based approach to address the molecular mechanism of childhood asthma with immunoglobulin E (IgE) or allergen sensitization related to microbiome in the airways remains lacking. ...Fifty-three children with lowly sensitized non-atopic asthma (n = 15), highly sensitized atopic asthma (n = 13), and healthy controls (n = 25) were enrolled. Blood metabolomic analysis with
H-nuclear magnetic resonance (NMR) spectroscopy and airway microbiome composition analysis by bacterial 16S rRNA sequencing were performed. An integrative analysis of their associations with allergen-specific IgE levels for lowly and highly sensitized asthma was also assessed. Four metabolites including tyrosine, isovalerate, glycine, and histidine were uniquely associated with lowly sensitized asthma, whereas one metabolite, acetic acid, was strongly associated with highly sensitized asthma. Metabolites associated with highly sensitized asthma (valine, isobutyric acid, and acetic acid) and lowly sensitized asthma (isovalerate, tyrosine, and histidine) were strongly correlated each other (P < 0.01). Highly sensitized asthma associated metabolites were mainly enriched in pyruvate and acetyl-CoA metabolisms. Metabolites associated with highly sensitized atopic asthma were mostly correlated with microbiota in the airways. Acetic acid, a short-chain fatty acid (SCFA), was negatively correlated with the genus Atopobium (P < 0.01), but positively correlated with the genus Fusobacterium (P < 0.05). In conclusion, metabolomics reveals microbes-related metabolic pathways associated with IgE responses to house dust mite allergens in childhood asthma. A strong correlation of metabolites related to highly sensitized atopic asthma with airway microbiota provides linkages between the host-microbial interactions and asthma endotypes.
Abstract
The aim of the study was to investigate differences in metabolic profiles between patients with major depressive disorder (MDD) with full remission (FR) and healthy controls (HCs). A total ...of 119 age-matched MDD patients with FR (
n
= 47) and HCs (
n
= 72) were enrolled and randomly split into training and testing sets. A
1
H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomics approach was used to identify differences in expressions of plasma metabolite biomarkers. Eight metabolites, including histidine, succinic acid, proline, acetic acid, creatine, glutamine, glycine, and pyruvic acid, were significantly differentially-expressed in the MDD patients with FR in comparison with the HCs. Metabolic pathway analysis revealed that pyruvate metabolism via the tricarboxylic acid cycle linked to amino acid metabolism was significantly associated with the MDD patients with FR. An algorithm based on these metabolites employing a linear support vector machine differentiated the MDD patients with FR from the HCs with a predictive accuracy, sensitivity, and specificity of nearly 0.85. A metabolomics-based approach could effectively differentiate MDD patients with FR from HCs. Metabolomic signatures might exist long-term in MDD patients, with metabolic impacts on physical health even in patients with FR.
Macroautophagy/autophagy can enable cancer cells to withstand cellular stress and maintain bioenergetic homeostasis by sequestering cellular components into newly formed double-membrane vesicles ...destined for lysosomal degradation, potentially affecting the efficacy of anti-cancer treatments. Using
13
C-labeled choline and
13
C-magnetic resonance spectroscopy and western blotting, we show increased de novo choline phospholipid (ChoPL) production and activation of PCYT1A (phosphate cytidylyltransferase 1, choline, alpha), the rate-limiting enzyme of phosphatidylcholine (PtdCho) synthesis, during autophagy. We also discovered that the loss of PCYT1A activity results in compromised autophagosome formation and maintenance in autophagic cells. Direct tracing of ChoPLs with fluorescence and immunogold labeling imaging revealed the incorporation of newly synthesized ChoPLs into autophagosomal membranes, endoplasmic reticulum (ER) and mitochondria during anticancer drug-induced autophagy. Significant increase in the colocalization of fluorescence signals from the newly synthesized ChoPLs and mCherry-MAP1LC3/LC3 (microtubule-associated protein 1 light chain 3) was also found on autophagosomes accumulating in cells treated with autophagy-modulating compounds. Interestingly, cells undergoing active autophagy had an altered ChoPL profile, with longer and more unsaturated fatty acid/alcohol chains detected. Our data suggest that de novo synthesis may be required to increase autophagosomal ChoPL content and alter its composition, together with replacing phospholipids consumed from other organelles during autophagosome formation and turnover. This addiction to de novo ChoPL synthesis and the critical role of PCYT1A may lead to development of agents targeting autophagy-induced drug resistance. In addition, fluorescence imaging of choline phospholipids could provide a useful way to visualize autophagosomes in cells and tissues.
AKT: AKT serine/threonine kinase; BAX: BCL2 associated X, apoptosis regulator; BECN1: beclin 1; ChoPL: choline phospholipid; CHKA: choline kinase alpha; CHPT1: choline phosphotransferase 1; CTCF: corrected total cell fluorescence; CTP: cytidine-5ʹ-triphosphate; DCA: dichloroacetate; DMEM: dulbeccos modified Eagles medium; DMSO: dimethyl sulfoxide; EDTA: ethylenediaminetetraacetic acid; ER: endoplasmic reticulum; GDPD5: glycerophosphodiester phosphodiesterase domain containing 5; GFP: green fluorescent protein; GPC: glycerophosphorylcholine; HBSS: hanks balances salt solution; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; LPCAT1: lysophosphatidylcholine acyltransferase 1; LysoPtdCho: lysophosphatidylcholine; MRS: magnetic resonance spectroscopy; MTORC1: mechanistic target of rapamycin kinase complex 1; PCho: phosphocholine; PCYT: choline phosphate cytidylyltransferase; PLA2: phospholipase A2; PLB: phospholipase B; PLC: phospholipase C; PLD: phospholipase D; PCYT1A: phosphate cytidylyltransferase 1, choline, alpha; PI3K: phosphoinositide-3-kinase; pMAFs: pancreatic mouse adult fibroblasts; PNPLA6: patatin like phospholipase domain containing 6; Pro-Cho: propargylcholine; Pro-ChoPLs: propargylcholine phospholipids; PtdCho: phosphatidylcholine; PtdEth: phosphatidylethanolamine; PtdIns3P: phosphatidylinositol-3-phosphate; RPS6: ribosomal protein S6; SCD: stearoyl-CoA desaturase; SEM: standard error of the mean; SM: sphingomyelin; SMPD1/SMase: sphingomyelin phosphodiesterase 1, acid lysosomal; SGMS: sphingomyelin synthase; WT: wild-type
Objectives
To evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader.
Methods
This ...single-centre retrospective study screened 21,150 consecutive body CT studies from September 2018 to February 2019. Pulmonary nodules detected by the DLS on axial CT images but not mentioned in initial radiology reports were flagged. Flagged images were scored by four board-certificated radiologists each with at least 5 years of experience. Nodules with scores of 2 (understandable miss) or 3 (should not be missed) were then categorised as unlikely to be clinically significant (2a or 3a) or likely to be clinically significant (2b or 3b) according to the 2017 Fleischner guidelines for pulmonary nodules. The miss rate was defined as the total number of studies receiving scores of 2 or 3 divided by total screened studies.
Results
Among 172 nodules flagged by the DLS, 60 (35%) missed nodules were confirmed by the radiologists. The nodules were further categorised as 2a, 2b, 3a, and 3b in 24, 14, 10, and 12 studies, respectively, with an overall positive predictive value of 35%. Missed pulmonary nodules were identified in 0.3% of all CT images, and one-third of these lesions were considered clinically significant.
Conclusions
Use of DLS-assisted automated detection as a second reader can identify missed pulmonary nodules, some of which may be clinically significant.
Clinical relevance/application.
Use of DLS to help radiologists detect pulmonary lesions may improve patient care.
Key Points
•
DLS-assisted automated detection as a second reader is feasible in a large consecutive cohort.
•
Performance of combined radiologists and DLS was better than DLS or radiologists alone.
•
Pulmonary nodules were missed more frequently in abdomino-pelvis CT than the thoracic CT.
Epidermal growth factor receptor (
) triple mutations with exon 19 deletion (del19), T790M, and cis-C797S (del19/T790M/cis-C797S mutations) frequently occur in patients with non-small cell lung ...cancer (NSCLC), while progression to frontline
-tyrosine kinase inhibitors (TKIs) and osimertinib was resistant to all clinically available
-TKIs. Brigatinib monotherapy may be a potential treatment for NSCLC harboring del19/T790M/cis-C797S mutations based on preclinical studies; however, no clinical report has evaluated its efficacy on
del19/T790M/cis-C797S mutations. Herein, we present a case of a female patient with
del19-mutated NSCLC treated with afatinib followed by osimertinib due to acquired T790M mutation. The
del19/T790M/cis-C797S mutations were detected following osimertinib treatment. Complete response of skull metastasis was confirmed after brigatinib treatment (90 mg daily). Unfortunately, she experienced intolerable adverse events; therefore, brigatinib was discontinued after three-month usage. This report provides the first reported evidence for the use of brigatinib monotherapy in patients with NSCLC harboring
del19/T790M/cis-C797S mutations after progression to previous
-TKIs.
To study the physiological roles of NADH and NADPH homeostasis in cancer, we studied the effect of NNT knockdown on physiology of SK-Hep1 cells. NNT knockdown cells show limited abilities to maintain ...NAD
and NADPH levels and have reduced proliferation and tumorigenicity. There is an increased dependence of energy production on oxidative phosphorylation. Studies with stable isotope tracers have revealed that under the new steady-state metabolic condition, the fluxes of TCA and glycolysis decrease while that of reductive carboxylation increases. Increased α-ketoglutarate/succinate ratio in NNT-deficient cells results in decrease in HIF-1α level and expression of HIF-1α regulated genes. Reduction in NADPH level leads to repression of HDAC1 activity and an increase in p53 acetylation. These findings suggest that NNT is essential to homeostasis of NADH and NADPH pools, anomalies of which affect HIF-1α- and HDAC1-dependent pathways, and hence retrograde response of mitochondria.
Uterine leiomyoma, also known as uterine fibroid, is the most common gynecological tumor, affecting almost 80% of women at some point during their lives. In the same time, other fibroid-like tumors ...have similar clinical presentations and about 0.5% of resected tumors of which were presumed benign fibroids in the preoperative diagnosis revealed as malignant sarcomas in the final histopathological examination. Amid the emergence of nonsurgical or minimally invasive procedures for symptomatic benign uterine fibroids, such as uterine artery embolization, high-intensity-focused ultrasound, or laparoscopic myomectomy, the preoperative diagnosis of uterine tumors through imaging becomes all the more relevant. Preoperative tissue sampling is challenging because of the variable location of the myometrial mass; thus, the preoperative evaluation of size and location is increasingly performed through magnetic resonance imaging. Features in images might also be useful for examining the full spectrum of such growths, from benign fibroids to neoplasms of uncertain behavior and malignant sarcomas. Benign fibroids include usual-type leiomyomas, myomas with degeneration, and mitotically active leiomyomas. Neoplasms of uncertain behavior include smooth muscle tumors of uncertain malignant potential, leiomyomas with bizarre nuclei, and cellular leiomyomas. Malignant sarcomas comprise leiomyosarcomas, endometrial stromal sarcomas, adenosarcomas, and carcinosarcomas. The purpose of this article is to review the spectrum of MRI findings of uterine fibroid-like tumors, from benign variants, uncertain behavior to malignant sarcomas, and update the advanced imaging modalities, including diffusion-weighted imaging, positron emission tomography/computed tomography, combining texture analysis and radiomics, to tackle this important issue.
Graphical abstract
The association between immune-related adverse events (irAEs) and survival outcomes in patients with advanced melanoma receiving therapy with immune checkpoint inhibitors (ICIs) has not been well ...established, particularly in Asian melanoma.
We retrospectively reviewed 49 melanoma patients undergoing therapy with ICIs (anti-PD-1 monotherapy), and analyzed the correlation between irAEs and clinical outcomes including progression-free survival (PFS) and overall survival (OS).
Overall, the patients who experienced grade 1-2 irAEs had longer PFS (median PFS, 4.6 vs. 2.5 months; HR, 0.52; 95% CI: 0.27-0.98; p = 0.042) and OS (median OS, 15.2 vs. 5.7 months; HR, 0.50; 95% CI: 0.24-1.02; p = 0.058) than the patients who did not experience irAEs. Regarding the type of irAE, the patients with either skin/vitiligo or endocrine irAEs showed better PFS (median PFS, 6.1 vs. 2.7 months; HR, 0.40, 95% CI: 0.21-0.74; p = 0.003) and OS (median OS, 18.7 vs. 4.5 months; HR, 0.34, 95% CI: 0.17-0.69, p = 0.003) than patients without any of these irAEs.
Melanoma patients undergoing anti-PD-1 monotherapy and experiencing mild-to-moderate irAEs (grade 1-2), particularly skin (vitiligo)/endocrine irAEs had favorable survival outcomes. Therefore, the association between irAEs and the clinical outcomes in melanoma patients undergoing anti-PD-1 ICIs may be severity and type dependent.
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