US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. Materials and Methods US images of thyroid nodules with pathologic results were retrospectively collected from a tertiary referral hospital between July 2022 and December 2022 and used to evaluate malignancy diagnoses generated by three LLMs-OpenAI's ChatGPT 3.5, ChatGPT 4.0, and Google's Bard. Inter- and intra-LLM agreement of diagnosis were evaluated. Then, diagnostic performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), was evaluated and compared for the LLMs and three interactive approaches: human reader combined with LLMs, image-to-text model combined with LLMs, and an end-to-end convolutional neural network model. Results A total of 1161 US images of thyroid nodules (498 benign, 663 malignant) from 725 patients (mean age, 42.2 years ± 14.1 [SD]; 516 women) were evaluated. ChatGPT 4.0 and Bard displayed substantial to almost perfect intra-LLM agreement (κ range, 0.65-0.86 [95% CI: 0.64, 0.86]), while ChatGPT 3.5 showed fair to substantial agreement (κ range, 0.36-0.68 [95% CI: 0.36, 0.68]). ChatGPT 4.0 had an accuracy of 78%-86% (95% CI: 76%, 88%) and sensitivity of 86%-95% (95% CI: 83%, 96%), compared with 74%-86% (95% CI: 71%, 88%) and 74%-91% (95% CI: 71%, 93%), respectively, for Bard. Moreover, with ChatGPT 4.0, the image-to-text-LLM strategy exhibited an AUC (0.83 [95% CI: 0.80, 0.85]) and accuracy (84% [95% CI: 82%, 86%]) comparable to those of the human-LLM interaction strategy with two senior readers and one junior reader and exceeding those of the human-LLM interaction strategy with one junior reader. Conclusion LLMs, particularly integrated with image-to-text approaches, show potential in enhancing diagnostic medical imaging. ChatGPT 4.0 was optimal for consistency and diagnostic accuracy when compared with Bard and ChatGPT 3.5. © RSNA, 2024 Supplemental material is available for this article.

Surveillance for malignant progression of LI-RADS version 2017 category 3/4 nodules using contrast-enhanced ultrasound.

OBJECTIVES: To identify the risk factors for predicting the malignant progression of LR-3/4 observations on the baseline and contrast-enhanced ultrasound (CEUS). METHODS: In total, 245 liver nodules assigned to LR-3/4 in 192 patients from January 2010 to December 2016 were followed up by baseline US and CEUS. The differences in the rate and time of progression to hepatocellular carcinoma (HCC) among subcategories (defined as P1-P7) of LR-3/4 in CEUS Liver Imaging Reporting and Data System (LI-RADS) were analyzed. The risk factors to predict progression to HCC were analyzed by univariate and multivariate Cox proportional hazard model analysis. RESULTS: A total of 40.3% of LR-3 nodules and 78.9% of LR-4 nodules eventually progressed to HCC. The cumulative incidence of progression was significantly higher for LR-4 than LR-3 (p < 0.001). The rate of progression was 81.2% in nodules with arterial phase hyperenhancement (APHE), 64.7% in nodules with late and mild washout, and 100% in nodules with both characteristics. The overall progression rate and median progression time of subcategory P1 nodules (LR-3a) were lower (38.0% vs. 47.6-100.0%) and later (25.1 months vs. 2.0-16.3 months) than those of other subcategories. The cumulative incidence of progression of LR-3a (P1), LR-3b (P2/3/4), and LR-4 (P5/6/7) categories were 38.0%, 52.9%, and 78.9%. The risk factors of HCC progression were Visualization score B/C, CEUS characteristics (APHE, washout), LR-4 classification, echo changes, and definite growth. CONCLUSION: CEUS is a useful surveillance tool for nodules at risk of HCC. CEUS characteristics, LI-RADS classification, and changes in nodules provide useful information for the progress of LR-3/4 nodules. CLINICAL RELEVANCE STATEMENT: CEUS characteristics, LI-RADS classification, and nodule changes provide important predictions for LR-3/4 nodule progression to HCC, which may stratify the risk of malignant progression to provide a more optimized and refined, more cost-effective, and time-efficient management strategy for patients. KEY POINTS: • CEUS is a useful surveillance tool for nodules at risk of HCC, CEUS LI-RADS successfully stratified the risks that progress to HCC. • CEUS characteristics, LI-RADS classification, and changes in nodules can provide important information on the progression of LR-3/4 nodules, which may be helpful for a more optimized and refined management strategy.

US LI-RADS in surveillance for recurrent hepatocellular carcinoma after curative treatment.

OBJECTIVES: To investigate the performance of US LI-RADS in surveillance for recurrent hepatocellular carcinoma (RHCC) after curative treatment. MATERIALS AND METHODS: This study enrolled 644 patients between January 2018 and August 2018 as a derivation cohort, and 397 patients from September 2018 to December 2018 as a validation cohort. The US surveillance after HCC curative treatment was performed. The US LI-RADS observation categories and visualization scores were analyzed. Four criteria using US LI-RADS or Alpha-fetoprotein (AFP) as the surveillance algorithm were evaluated. The sensitivity, specificity, and negative predictive value (NPV) were calculated. RESULTS: A total of 212 (32.9%) patients in derivation cohort and 158 (39.8%) patients in validation cohort were detected to have RHCCs. The criterion of US-2/3 or AFP ≥ 20 µg/L had higher sensitivity (derivation, 96.7% vs 92.9% vs 81.1% vs 90.6%; validation, 96.2% vs 90.5% vs 80.4% vs 89.9%) and NPV (derivation, 95.7% vs 93.3% vs 88.0% vs 91.8%; validation, 94.6% vs 89.4% vs 83.6% vs 89.0%), but lower specificity (derivation, 35.9% vs 48.2% vs 67.6% vs 51.9%; validation, 43.5% vs 52.7% vs 66.1% vs 54.0%) than criterion of US-2/3, US-3, and US-3 or AFP ≥ 20 µg/L. Analysis of the visualization score subgroups confirmed that the sensitivity (89.2-97.6% vs 81.0-83.3%) and NPV(88.4-98.0% vs 80.0-83.3%) of score A and score B groups were higher than score C group in criterion of US-2/3 in both two cohorts. CONCLUSIONS: In the surveillance for RHCC, US LI-RADS with AFP had a high sensitivity and NPV when US-2/3 or AFP ≥ 20 µg/L was considered a criterion. CLINICAL RELEVANCE STATEMENT: The criterion of US-2/3 or AFP ≥ 20 µg/L improves sensitivity and NPV for RHCC surveillance, which provides a valuable reference for patients in RHCC surveillance after curative treatment. KEY POINTS: • US LI-RADS with AFP had high sensitivity and NPV in surveillance for RHCC when considering US-2/3 or AFP ≥ 20 µg/L as a criterion. • After US with AFP surveillance, patients with US-2/3 or AFP ≥ 20 µg/L should perform enhanced imaging for confirmative diagnosis. Patients with US-1 or AFP < 20 µg/L continue to repeat US with AFP surveillance. • Patients with risk factors for poor visualization scores limited the sensitivity of US surveillance in RHCC.

Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management.

Importance: To optimize the integration of artificial intelligence (AI) decision aids and reduce workload in thyroid nodule management, it is critical to incorporate personalized AI into the decision-making processes of radiologists with varying levels of expertise. Objective: To develop an optimized integration of AI decision aids for reducing radiologists' workload while maintaining diagnostic performance compared with traditional AI-assisted strategy. Design, Setting, and Participants: In this diagnostic study, a retrospective set of 1754 ultrasonographic images of 1048 patients with 1754 thyroid nodules from July 1, 2018, to July 31, 2019, was used to build an optimized strategy based on how 16 junior and senior radiologists incorporated AI-assisted diagnosis results with different image features. In the prospective set of this diagnostic study, 300 ultrasonographic images of 268 patients with 300 thyroid nodules from May 1 to December 31, 2021, were used to compare the optimized strategy with the traditional all-AI strategy in terms of diagnostic performance and workload reduction. Data analyses were completed in September 2022. Main Outcomes and Measures: The retrospective set of images was used to develop an optimized integration of AI decision aids for junior and senior radiologists based on the selection of AI-assisted significant or nonsignificant features. In the prospective set of images, the diagnostic performance, time-based cost, and assisted diagnosis were compared between the optimized strategy and the traditional all-AI strategy. Results: The retrospective set included 1754 ultrasonographic images from 1048 patients (mean [SD] age, 42.1 [13.2] years; 749 women [71.5%]) with 1754 thyroid nodules (mean [SD] size, 16.4 [10.6] mm); 748 nodules (42.6%) were benign, and 1006 (57.4%) were malignant. The prospective set included 300 ultrasonographic images from 268 patients (mean [SD] age, 41.7 [14.1] years; 194 women [72.4%]) with 300 thyroid nodules (mean [SD] size, 17.2 [6.8] mm); 125 nodules (41.7%) were benign, and 175 (58.3%) were malignant. For junior radiologists, the ultrasonographic features that were not improved by AI assistance included cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, and nodules smaller than 5 mm, whereas for senior radiologists the features that were not improved by AI assistance were cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, very hypoechoic nodules, nodules taller than wide, lobulated or irregular nodules, and extrathyroidal extension. Compared with the traditional all-AI strategy, the optimized strategy was associated with increased mean task completion times for junior radiologists (reader 11, from 15.2 seconds [95% CI, 13.2-17.2 seconds] to 19.4 seconds [95% CI, 15.6-23.3 seconds]; reader 12, from 12.7 seconds [95% CI, 11.4-13.9 seconds] to 15.6 seconds [95% CI, 13.6-17.7 seconds]), but shorter times for senior radiologists (reader 14, from 19.4 seconds [95% CI, 18.1-20.7 seconds] to 16.8 seconds [95% CI, 15.3-18.3 seconds]; reader 16, from 12.5 seconds [95% CI, 12.1-12.9 seconds] to 10.0 seconds [95% CI, 9.5-10.5 seconds]). There was no significant difference in sensitivity (range, 91%-100%) or specificity (range, 94%-98%) between the 2 strategies for readers 11 to 16. Conclusions and Relevance: This diagnostic study suggests that an optimized AI strategy in thyroid nodule management may reduce diagnostic time-based costs without sacrificing diagnostic accuracy for senior radiologists, while the traditional all-AI strategy may still be more beneficial for junior radiologists.

Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions.

PURPOSE: To establish shear-wave elastography (SWE) combined with contrast-enhanced ultrasound (CEUS) algorithm (SCCA) and improve the diagnostic performance in differentiating focal liver lesions (FLLs). MATERIAL AND METHODS: We retrospectively selected patients with FLLs between January 2018 and December 2019 at the First Affiliated Hospital of Sun Yat-sen University. Histopathology was used as a standard criterion except for hemangiomas and focal nodular hyperplasia. CEUS with SonoVue (Bracco Imaging) and SCCA combining CEUS and maximum value of elastography with < 20 kPa and > 90 kPa thresholds were used for the diagnosis of FLLs. The diagnostic performance of CEUS and SCCA was calculated and compared. RESULTS: A total of 171 FLLs were included, with 124 malignant FLLs and 47 benign FLLs. The area under curve (AUC), sensitivity, and specificity in detecting malignant FLLs were 0.83, 91.94%, and 74.47% for CEUS, respectively, and 0.89, 91.94%, and 85.11% for SCCA, respectively. The AUC of SCCA was significantly higher than that of CEUS (P = 0.019). Decision curves indicated that SCCA provided greater clinical benefits. The SCCA provided significantly improved prediction of clinical outcomes, with a net reclassification improvement index of 10.64% (P = 0.018) and integrated discrimination improvement of 0.106 (P = 0.019). For subgroup analysis, we divided the FLLs into a chronic-liver-disease group (n = 88 FLLs) and a normal-liver group (n = 83 FLLs) according to the liver background. In the chronic-liver-disease group, there were no differences between the CEUS-based and SCCA diagnoses. In the normal-liver group, the AUC of SCCA and CEUS in the characterization of FLLs were 0.89 and 0.83, respectively (P = 0.018). CONCLUSION: SCCA is a feasible tool for differentiating FLLs in patients with normal liver backgrounds. Further investigations are necessary to validate the universality of this algorithm.

Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps.

OBJECTIVE: The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience. METHODS: We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3. RESULTS: A total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05). CONCLUSION: The performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.

Preoperative Survival Prediction in Intrahepatic Cholangiocarcinoma Using an Ultrasound-Based Radiographic-Radiomics Signature.

OBJECTIVES: To construct a preoperative model for survival prediction in intrahepatic cholangiocarcinoma (ICC) patients using ultrasound (US) based radiographic-radiomics signatures. METHODS: Between April 2010 and September 2015, 170 patients with ICC who underwent curative resection were retrospectively recruited. Overall survival (OS)-related radiographic signatures and radiomics signatures based on preoperative US were built and assessed through a time-dependent receiver operating characteristic curve analysis. A nomogram was developed based on the selected predictors from the radiographic-radiomics signatures and clinical and laboratory results of the training cohort (n = 127), validated in an independent testing cohort (n = 43) by the concordance index (C-index), and compared with the Tumor Node Metastasis (TNM) cancer staging system as well as the radiographic and radiomics nomograms. RESULTS: The median areas under the curve of the radiomics signature and radiographic signature were higher than that of the TNM staging system in the testing cohort, although the values were not significantly different (0.76-0.82 versus 0.62, P = .485 and .264). The preoperative nomogram with CA 19-9, sex, ascites, radiomics signature, and radiographic signature had C-indexes of 0.72 and 0.75 in the training and testing cohorts, respectively, and it had significantly higher predictive performance than the 8th TNM staging system in the testing cohort (C-index: 0.75 versus 0.67, P = .004) and a higher C-index than the radiomics nomograms (0.75 versus 0.68, P = .044). CONCLUSIONS: The preoperative nomogram integrated with the radiographic-radiomics signature demonstrated good predictive performance for OS in ICC and was superior to the 8th TNM staging system.

LR-M Observations on Contrast-Enhanced Ultrasound: Detection of Hepatocellular Carcinoma Using Additional Features in Comparison With Current LI-RADS Criteria.

BACKGROUND. Contrast-enhanced ultrasound (CEUS) LI-RADS assigns category LR-M to observations that are definitely or probably malignant but that on imaging are not specific for hepatocellular carcinoma (HCC). A high percentage of LR-M observations represent HCC. OBJECTIVE. The purpose of this study was to retrospectively evaluate the utility of additional features, beyond conventional LI-RADS major features, for detecting HCC among LR-M observations on CEUS. METHODS. This retrospective study included 174 patients (145 men, 29 women; mean age, 53 years) at high risk of HCC who underwent CEUS from August 2014 to June 2016 that showed an LR-M observation according to CEUS LI-RADS version 2017. Two radiologists independently assessed CEUS images for major features and four additional features (chaotic vessels, peripheral circular artery, clear boundary of tumor enhancement, clear boundary of intratumoral unenhanced area). The diagnostic performance of four proposed criteria for the detection of HCC among LR-M observations was assessed. The impact of criteria based on the additional findings on detection of HCC was further explored. Histology or composite imaging and clinical follow-up were the reference standards. RESULTS. The 174 LR-M observations included 142 HCCs and 32 non-HCC lesions (20 intrahepatic cholangiocarcinomas, five combined hepatocellular-cholangiocarcinomas, seven benign lesions). Interreader agreement on the additional features ranged from κ = 0.65 to κ = 0.88. Two of the additional features had excellent PPV for HCC: chaotic vessels (94.8%) and peripheral circular arteries (98.1%). The presence of either of these two additional features had sensitivity of 50.7%, specificity of 90.6%, PPV of 96.0%, and NPV of 29.3% for HCC. Three other criteria incorporating variations of major LI-RADS features but not the additional features had sensitivities of 55.6-96.5%, specificities of 49.6-68.8%, PPVs of 87.8-90.6%, and NPVs of 25.0-75.0%. On the basis of criteria that included additional features, 75 of 174 LR-M observations were recategorized LR-5; 72 of the 75 were HCC. CONCLUSION. The presence of chaotic vessels and/or peripheral circular artery had high specificity and PPV for HCC among LR-M observations. Other explored criteria based on major features did not have higher specificity or PPV. CLINICAL IMPACT. Clinical adoption of the additional CEUS features could help establish the diagnosis of HCC noninvasively and avoid the need for biopsy of LR-M observations.

Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation.

OBJECTIVES: This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation. METHODS: This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images-grayscale ultrasound, arterial phase, portal venous phase and delayed phase-were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified. RESULTS: The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2-year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001). CONCLUSION: These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation. ADVANCES IN KNOWLEDGE: CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates, respectively, 76.5% and 9.5% (p < 0.0001).

Roles of Major RNA Adenosine Modifications in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer malignancy worldwide and is known to have poor prognosis. The pathogenesis behind the development of HNSCC is not fully understood. Modifications on RNA are involved in many pathophysiological processes, such as tumor development and inflammation. Adenosine-related RNA modifications have shown to be linked to cancer and may play a role in cancer occurrence and development. To date, there are at least 170 different chemical RNA modifications that modify coding and non-coding RNAs (ncRNAs). These modifications affect RNA stability and transcription efficiency. In this review, we focus on the current understanding of the four major RNA adenosine modifications (N6-Methyladenosine, N1-Methyladenosine, Alternative Polyadenylation Modification and A-to-I RNA editing) and their potential molecular mechanisms related to HNSCC development and progression. We also touch on how these RNA modifications affect treatment of HNSCCs.

RGB Three-Channel SWE-Based Ultrasomics Model: Improving the Efficiency in Differentiating Focal Liver Lesions.

OBJECTIVE: To explore a new method for color image analysis of ultrasomics and investigate the efficiency in differentiating focal liver lesions (FLLs) by Red, Green, and Blue (RGB) three-channel SWE-based ultrasomics model. METHODS: One hundred thirty FLLs were randomly divided into training set (n = 65) and validation set (n = 65). The RGB three-channel and direct conversion methods were applied to the same color SWE images. Ultrasomics features were extracted from the preprocessing images establishing two feature data sets. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. Two models, named RGB model (based on RGB three-channel conversion) and direct model (based on direct conversion), were used to differentiate FLLs. The diagnosis performance of the two models was evaluated by area under the curve (AUC), calibration curves, decision curves, and net reclassification index (NRI). RESULTS: In the validation cohort, the AUC of the direct model and RGB model in characterization on FLLs were 0.813 and 0.926, respectively (p = 0.038). Calibration curves and decision curves indicated that the RGB model had better calibration efficiency and provided greater clinical benefits. NRI revealed that the RGB model correctly reclassified 7% of malignant cases and 25% of benign cases compared to the direct model (p = 0.01). CONCLUSION: The RGB model generated by RGB three-channel method yielded better diagnostic efficiency than the direct model established by direct conversion method. The RGB three-channel method may be promising on ultrasomics analysis of color images in clinical application.

Luminescent ruffled iridium(iii) porphyrin complexes containing N-heterocyclic carbene ligands: structures, spectroscopies and potent antitumor activities under dark and light irradiation conditions.

A panel of iridium(iii) porphyrin complexes containing axial N-heterocyclic carbene (NHC) ligand(s) were synthesized and characterized. X-ray crystal structures of the bis-NHC complexes [IrIII(ttp)(IMe)2]+ (2a), [IrIII(oep)(BIMe)2]+ (2d), [IrIII(oep)(I i Pr)2]+ (2e) and [IrIII(F20tpp)(IMe)2]+ (2f) display ruffled porphyrin rings with mesocarbon displacements of 0.483-0.594 Å and long Ir-CNHC bonds of 2.100-2.152 Å. Variable-temperature 1H NMR analysis of 2a reveals that the macrocycle porphyrin ring inversion takes place in solution with an activation barrier of 40 ± 1 kJ mol-1. The UV-vis absorption spectra of IrIII(por)-NHC complexes display split Soret bands. TD-DFT calculations and resonance Raman experiments show that the higher-energy Soret band is derived from the 1MLCT dπ(Ir) → π*(por) transition. The near-infrared phosphorescence of IrIII(por)-NHC complexes from the porphyrin-based 3(π, π*) state features broad emission bands at 701-754 nm with low emission quantum yields and short lifetimes (Φ em < 0.01; τ < 4 µs). [IrIII(por)(IMe)2]+ complexes (por = ttp and oep) are efficient photosensitizers for 1O2 generation (Φ so = 0.64 and 0.88) and are catalytically active in the light-induced aerobic oxidation of secondary amines and arylboronic acid. The bis-NHC complexes exhibit potent dark cytotoxicity towards a panel of cancer cells with IC50 values at submicromolar levels. The cytotoxicity of these complexes could be further enhanced upon light irradiation with IC50 values as low as nanomolar levels in association with the light-induced generation of reactive oxygen species (ROS). Bioimaging of [IrIII(oep)(IMe)2]+ (2c) treated cells indicates that this Ir complex mainly targets the endoplasmic reticulum. [IrIII(oep)(IMe)2]+ catalyzes the photoinduced generation of singlet oxygen and triggers protein oxidation, cell cycle arrest, apoptosis and the inhibition of angiogenesis. It also causes pronounced photoinduced inhibition of tumor growth in a mouse model of human cancer.

Dynamics of Oxygen-Independent Photocleavage of Blebbistatin as a One-Photon Blue or Two-Photon Near-Infrared Light-Gated Hydroxyl Radical Photocage.

Development of versatile, chemically tunable photocages for photoactivated chemotherapy (PACT) represents an excellent opportunity to address the technical drawbacks of conventional photodynamic therapy (PDT) whose oxygen-dependent nature renders it inadequate in certain therapy contexts such as hypoxic tumors. As an alternative to PDT, oxygen free mechanisms to generate cytotoxic reactive oxygen species (ROS) by visible light cleavable photocages are in demand. Here, we report the detailed mechanisms by which the small molecule blebbistatin acts as a one-photon blue light-gated or two-photon near-infrared light-gated photocage to directly release a hydroxyl radical (•OH) in the absence of oxygen. By using femtosecond transient absorption spectroscopy and chemoselective ROS fluorescent probes, we analyze the dynamics and fate of blebbistatin during photolysis under blue light. Water-dependent photochemistry reveals a critical process of water-assisted protonation and excited state intramolecular proton transfer (ESIPT) that drives the formation of short-lived intermediates, which surprisingly culminates in the release of •OH but not superoxide or singlet oxygen from blebbistatin. CASPT2//CASSCF calculations confirm that hydrogen bonding between water and blebbistatin underpins this process. We further determine that blue light enables blebbistatin to induce mitochondria-dependent apoptosis, an attribute conducive to PACT development. Our work demonstrates blebbistatin as a controllable photocage for •OH generation and provides insight into the potential development of novel PACT agents.

Enhanced Drug Photosafety by Interchromophoric Interaction Owing to Intramolecular Charge Separation.

Imatinib is a synthetic tyrosinase inhibitor that is employed for the treatment of some kinds of human cancer. This drug has a low phototoxicity towards DNA, but its pyridylpyrimidine (1) fragment by itself exhibits significant phototoxicitiy. The intrinsic mechanism that leads to the enhanced photosafety of Imatinib is not yet known. Here, the properties of the excited state and interchromophoric interactions of Imatinib have been explored by using ultrafast laser flash photolysis and agarose electrophoresis studies. An intramolecular charge separation was directly observed for the irradiated Imatinib, which accounts for the relaxation of its excited state. An anionic form of pyridylpyrimidine (1) was deduced from the results of time-resolved resonance Raman spectra and by quenching experimental studies on compound 1 and diaminotoluene. In contrast, compound 1 efficiently transformed into triplet excited states with a long lifetime, which explained the phototoxicity associated with this fragment. This work provides insight into how to design drugs with lower phototoxicitiy or improved photostability by using interchromophoric interactions.

pH Dependent Photodeprotection of Formaldehyde: Homolytic C-C Scission in Acidic Aqueous Solution versus Heterolytic C-C Scission in Basic Aqueous Solution.

The photodeprotection of formaldehyde was investigated for 3-(1-hydroxypropan-2-yl)benzophenone (3-HPBP) with ultrafast time-resolved spectroscopy. The femtosecond transient absorption results indicated the singlet excited state of 3-HPBP transformed efficiently into its triplet state by a fast intersystem crossing. In acidic (pH = 0) and basic (pH = 12.5) aqueous solutions, the triplet intermediate was a key precursor for the deprotection of formaldehyde via two different pathways. However, little photodeprotection was observed in neutral (pH = 7) aqueous solution where the triplet intermediate appeared to undergo a proton coupled electron transfer process to form a ketyl radical transient. The important benzylic biradical intermediates seen in the acidic and basic aqueous solutions were identified by time-resolved resonance Raman spectra whose vibrational frequency patterns were consistent with DFT calculation results for the benzylic biradical intermediate. The results here indicate that the ß-carbon alcohol group of the triplet state 3-HPBP is deprotonated in basic aqueous solutions and this leads to a heterolytic C-C bond cleavage to deprotect formaldehyde and produce the benzylic carbanion triplet state species, whereas protonation of the carbonyl moiety of the triplet state 3-HPBP leads to direct generation of a benzylic biradical intermediate and the deprotection of formaldehyde in acidic aqueous solutions via a homolytic C-C bond cleavage.

Ketyl Radical Formation via Proton-Coupled Electron Transfer in an Aqueous Solution versus Hydrogen Atom Transfer in Isopropanol after Photoexcitation of Aromatic Carbonyl Compounds.

The excited nπ* and ππ* triplets of two benzophenone (BP) and two anthraquinone (AQ) derivatives have been observed in acetonitrile, isopropanol, and mixed aqueous solutions using time-resolved resonance Raman spectroscopic and nanosecond transient absorption experiments. These experimental results, combined with results from density functional theory calculations, reveal the effects of solvent and substituents on the properties, relative energies, and chemical reactivities of the nπ* and ππ* triplets. The triplet nπ* configuration was found to act as the reactive species for a subsequent hydrogen atom transfer reaction to produce a ketyl radical intermediate in the isopropanol solvent, while the triplet ππ* undergoes a proton-coupled electron transfer (PCET) in aqueous solutions to produce a ketyl radical intermediate. This PCET reaction, which occurs via a concerted proton transfer (to the excited carbonyl group) and electron transfer (to the excited phenyl ring), can account for the experimental observation by several different research groups over the past 40 years of the formation of ketyl radicals after photolysis of a number of BP and AQ derivatives in aqueous solutions, although water is considered to be a relatively "inert" hydrogen-donor solvent.

Reaction mechanisms and structural characterization of the reactive intermediates observed after the photolysis of 3-(hydroxymethyl)benzophenone in acetonitrile, 2-propanol, and neutral and acidic aqueous solutions.

Nanosecond time-resolved resonance Raman (ns-TR(3)) spectroscopy was employed to investigate the photoinduced reactions of 3-(hydroxymethyl)benzophenone (1) in acetonitrile, 2-propanol, and neutral and acidic aqueous solutions. Density functional theory calculations were utilized to help the interpretation of the experimental spectra. In acetonitrile, the neutral triplet state 1 [denoted here as (m-BPOH)(3)] was observed on the nanosecond to microsecond time scale. In 2-propanol this triplet state appeared to abstract a hydrogen atom from the solvent molecules to produce the aryphenyl ketyl radical of 1 (denoted here as ArPK of 1), and then this species underwent a cross-coupling reaction with the dimethylketyl radical (also formed from the hydrogen abstraction reaction) to form a long-lived light absorbing transient species that was tentatively identified to be mainly 2-(4-(hydroxy(3-(hydroxymethyl)phenyl)methylene)cyclohexa-2,5-dienyl)propan-2-ol. In 1:1 H(2)O:CH(3)CN aqueous solution at neutral pH, (m-BPOH)(3) reacted with water to produce the ArPK of 1 and then underwent further reaction to produce a long-lived light absorbing transient species. Three photochemical reactions appeared to take place after 266 nm photolysis of 1 in acidic aqueous solutions, a photoreduction reaction, an overall photohydration reaction, and a novel photoredox reaction. TR(3) experiments in 1:1 H(2)O:CH(3)CN aqueous solution at pH 2 detected a new triplet biradical species, which is associated with an unusual photoredox reaction. This reaction is observed to be the predominant reaction at pH 2 and seems to face competition from the overall photohydration reaction at pH 0.