Abstract

Background

Large Language Models like Chat Generative Pre-trained Transformer (ChatGPT) have demonstrated potential for differential diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals, which may not accurately represent real-world clinical scenarios.

Purpose

This study aimed to assess the diagnostic capabilities of ChatGPT using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists.

Methods

We consecutively collected brain MRI reports from preoperative brain tumor patients at Osaka Metropolitan University Hospital, taken from January to December 2021. ChatGPT and five radiologists were presented with the same findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. Chi-square tests and Fisher’s exact test were used for statistical analysis.

Results

In a study analyzing 99 radiological reports, ChatGPT achieved a final diagnostic accuracy of 75% (95% CI: 66, 83%), while radiologists’ accuracy ranged from 64% to 82%. ChatGPT’s final diagnostic accuracy using reports from neuroradiologists was higher at 82% (95% CI: 71, 89%), compared to 52% (95% CI: 33, 71%) using those from general radiologists with a p-value of 0.012. In the realm of differential diagnoses, ChatGPT’s accuracy was 95% (95% CI: 91, 99%), while radiologists’ fell between 74% and 88%. Notably, for these differential diagnoses, ChatGPT’s accuracy remained consistent whether reports were from neuroradiologists (96%, 95% CI: 89, 99%) or general radiologists (91%, 95% CI: 73, 98%) with a p-value of 0.33.

Conclusion

ChatGPT exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. ChatGPT can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents, especially for understanding diagnostic cues and handling challenging cases.

Summary

This study evaluated ChatGPT’s diagnostic capabilities using real-world clinical MRI reports from brain tumor cases, revealing that its accuracy in interpreting brain tumors from MRI findings is competitive with radiologists.

Key results

ChatGPT demonstrated a diagnostic accuracy rate of 75% for final diagnoses based on preoperative MRI findings from 99 brain tumor cases, competing favorably with five radiologists whose accuracies ranged between 64% and 82%. For differential diagnoses, ChatGPT achieved a remarkable 95% accuracy, outperforming several of the radiologists.

Radiology reports from neuroradiologists and general radiologists showed varying accuracy when input into ChatGPT. Reports from neuroradiologists resulted in higher diagnostic accuracy for final diagnoses, while there was no difference in accuracy for differential diagnoses between neuroradiologists and general radiologists.

DOI： 10.1101/2023.10.27.23297585

Abstract

Purpose

To compare the diagnostic performance between Chat Generative Pre-trained Transformer (ChatGPT), based on the GPT-4 architecture, and radiologists from patient’s medical history and imaging findings in challenging neuroradiology cases.

Methods

We collected 30 consecutive “Freiburg Neuropathology Case Conference” cases from the journal Clinical Neuroradiology between March 2016 and June 2023. GPT-4 based ChatGPT generated diagnoses from the patient’s provided medical history and imaging findings for each case, and the diagnostic accuracy rate was determined based on the published ground truth. Three radiologists with different levels of experience (2, 4, and 7 years of experience, respectively) independently reviewed all the cases based on the patient’s provided medical history and imaging findings, and the diagnostic accuracy rates were evaluated. The Chi-square tests were performed to compare the diagnostic accuracy rates between ChatGPT and each radiologist.

Results

ChatGPT achieved an accuracy rate of 23% (7/30 cases). Radiologists achieved the following accuracy rates: a junior radiology resident had 27% (8/30) accuracy, a senior radiology resident had 30% (9/30) accuracy, and a board-certified radiologist had 47% (14/30) accuracy. ChatGPT’s diagnostic accuracy rate was lower than that of each radiologist, although the difference was not significant (p= 0.99, 0.77, and 0.10, respectively).

Conclusion

The diagnostic performance of GPT-4 based ChatGPT did not reach the performance level of either junior/senior radiology residents or board-certified radiologists in challenging neuroradiology cases. While ChatGPT holds great promise in the field of neuroradiology, radiologists should be aware of its current performance and limitations for optimal utilization.

DOI： 10.1101/2023.08.28.23294607

担当区分：筆頭著者   掲載種別：研究論文（学術雑誌）   国際・国内誌：国際誌  

DOI： 10.1148/radiol.223016

PubMed

掲載種別：研究論文（学術雑誌）  

DOI： 10.1002/jmri.28892

PubMed

掲載種別：研究論文（学術雑誌）   国際・国内誌：国際誌  

DOI： 10.1148/radiol.231040

PubMed

掲載種別：研究論文（学術雑誌）   国際・国内誌：国際誌  

DOI： 10.1007/s00234-023-03142-9

PubMed

Alzheimer病(AD)の大脳皮質中アミロイド-β沈着はアミロイドPETにより評価されるが、アミロイド-β関連鉄は位相差強調画像化法(PADRE)で検出される。本報ではPADREイメージングの低強度領域と、11C-Pittzburg compound Bを用いたアミロイドPET(11C-PiB PET)集積との相関性について検討した。2015年1月～2018年4月にかけ、11C-PiB PET検査を受けたAD患者8例(後向きに3例、前向きに5例)と年齢をマッチさせた健常対照者10例を対象に、楔部、楔前部、上前頭回(SFG)、上側頭回(STG)のROIを自動セグメント化した。視覚的評価による4点スケール法や体積の半定量評価(低強度領域の体積百分率)から、各ROIのPADREイメージングにおける低強度領域を評価し、アミロイドPETでは各ROIの平均SUV比(SUVR)を算出した。PADREイメージングにおける4点スケール法とアミロイドPETのSUVR間、体積の半定量評価とSUVR間におけるSpearman相関係数を評価した結果、楔前部では全例にPADREイメージングによる4点スケール法とアミロイドPETのSUVR間で有意な正相関が同定された。また、楔部では、全例にPADREイメージングの半定量評価による体積の百分率とアミロイドPETのSUVR間で、有意な正相関が認められた。以上より、アミロイド-β増強PADREイメージングは、特に楔部や楔前部ではアミロイドPETのSUVR予測に有用と考えられたことから、アミロイド沈着検出によるAD診断への適用が期待された。

掲載種別：研究論文（学術雑誌）  

DOI： 10.11318/mii.40.66

掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11604-022-01370-2

PubMed

その他URL： https://link.springer.com/article/10.1007/s11604-022-01370-2/fulltext.html

掲載種別：研究論文（学術雑誌）   国際・国内誌：国際誌  

DOI： 10.1007/s00234-022-02972-3

PubMed

国際・国内誌：国際誌  

Here, we present a very unusual case of orbital apex schwannoma with a high titer of proteinase 3 antineutrophil cytoplasmic antibody (PR3-ANCA). A 67-year-old man presented with a 3-month history of double vision. Radiological examinations revealed a mass lesion at the left orbital apex, and laboratory examination revealed a high titer of PR3-ANCA, of 49.1 U/mL (reference range<2.0). After the surgery, the lesion was histologically diagnosed as schwannoma, and the PR3-ANCA titer decreased to 8.4 U/m. Although making a correct diagnosis of orbital apex schwannoma may be difficult due to the need to differentiate from granulomatosis with polyangiitis when PR3-ANCA serum levels are elevated, careful examination of the radiological findings may aid the diagnosis.

DOI： 10.1016/j.radcr.2022.01.047

PubMed

担当区分：筆頭著者   国際・国内誌：国際誌  

Dysembryoplastic neuroepithelial tumors (DNTs) are benign brain tumors classified as grade 1 in the 2021 World Health Organization (WHO) classification of central nervous system tumors. DNTs rarely undergo malignant transformation and cause symptomatic intracranial hemorrhage. We report a case of malignant transformation of DNT presenting with intraventricular hemorrhage and review the literature on malignant transformation of DNTs. An 18-year-old woman with a history of epilepsy presented with a sudden headache and vomiting. Radiological examination revealed a mass lesion in the left parietal lobe and intraventricular hemorrhage. The patient underwent an emergency craniotomy for brain tumor resection. The lesion was pathologically diagnosed as a malignant transformation of DNT. She had been followed up without tumor recurrence for 2 years after surgery.

DOI： 10.1016/j.radcr.2022.01.014

PubMed

担当区分：筆頭著者   掲載種別：研究論文（学術雑誌）   国際・国内誌：国内誌  

PURPOSE: While amyloid-β deposition in the cerebral cortex for Alzheimer's disease (AD) is often evaluated by amyloid positron emission tomography (PET), amyloid-β-related iron can be detected using phase difference enhanced (PADRE) imaging; however, no study has validated the association between PADRE imaging and amyloid PET. This study investigated whether the degree of hypointense areas on PADRE imaging correlated with the uptake of amyloid PET. METHODS: PADRE imaging and amyloid PET were performed in 8 patients with AD and 10 age-matched normal controls. ROIs in the cuneus, precuneus, superior frontal gyrus (SFG), and superior temporal gyrus (STG) were automatically segmented. The degree of hypointense areas on PADRE imaging in each ROI was evaluated using 4-point scaling of visual assessment or volumetric semiquantitative assessment (the percentage of hypointense volume within each ROI). The mean standardized uptake value ratio (SUVR) of amyloid PET in each ROI was also calculated. The Spearman's correlation coefficient between the 4-point scale of PADRE imaging and SUVR of amyloid PET or between the semiquantitative hypointense volume percentage and SUVR in each ROI was evaluated. RESULTS: In the precuneus, a significant positive correlation was identified between the 4-point scale of PADRE imaging and SUVR of amyloid PET (Rs = 0.5; P = 0.034) in all subjects. In the cuneus, a significant positive correlation was identified between the semiquantitative volume percentage of PADRE imaging and SUVR of amyloid PET (Rs = 0.55; P = 0.02) in all subjects. CONCLUSION: Amyloid-β-enhancing PADRE imaging can be used to predict the SUVR of amyloid PET, especially in the cuneus and precuneus, and may have the potential to be used for diagnosing AD by detecting amyloid deposition.

DOI： 10.2463/mrms.mp.2021-0123

PubMed

掲載種別：研究論文（学術雑誌）   国際・国内誌：国際誌  

Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose To train and to validate a deep learning (DL)-based model to produce DSA-like cerebral angiograms directly from dynamic angiograms and then quantitatively and visually evaluate these angiograms for clinical usefulness. Materials and Methods A retrospective model development and validation study was conducted on dynamic and DSA image pairs consecutively collected from January 2019 through April 2019. Angiograms showing misregistration were first separated per patient by two radiologists and sorted into the misregistration test data set. Nonmisregistration angiograms were divided into development and external test data sets at a ratio of 8:1 per patient. The development data set was divided into training and validation data sets at ratio of 3:1 per patient. The DL model was created by using the training data set, tuned with the validation data set, and then evaluated quantitatively with the external test data set and visually with the misregistration test data set. Quantitative evaluations used the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) with mixed liner models. Visual evaluation was conducted by using a numerical rating scale. Results The training, validation, nonmisregistration test, and misregistration test data sets included 10 751, 2784, 1346, and 711 paired images collected from 40 patients (mean age, 62 years ± 11 [standard deviation]; 33 women). In the quantitative evaluation, DL-generated angiograms showed a mean PSNR value of 40.2 dB ± 4.05 and a mean SSIM value of 0.97 ± 0.02, indicating high coincidence with the paired DSA images. In the visual evaluation, the median ratings of the DL-generated angiograms were similar to or better than those of the original DSA images for all 24 sequences. Conclusion The deep learning-based model provided clinically useful cerebral angiograms free from clinically significant artifacts directly from dynamic angiograms. Published under a CC BY 4.0 license. Supplemental material is available for this article.

DOI： 10.1148/radiol.2021203692

PubMed

症例は50歳代女性で、突然の右側頭部痛があり、その後嘔吐を繰り返した。救急搬送時の単純CTで右側脳室三角部を中心とした4×3×3cm大の腫瘤性病変を認めた。腫瘤の形態は円形で境界明瞭であり、内部は均一かつ灰白質と等吸収で、石灰化は認めなかった。右側脳室内には出血を伴っていた。第6病日のMRIで右側脳室内腫瘤は、T1強調像で白質より軽度低信号、T2強調像で白質より高信号で、拡散強調像では高信号、ADC mapでは拡散は白質と同程度であった。磁化率強調像では腫瘤辺縁に点状または線状の低信号を認め、出血と考えられた。腫瘤は均一な造影効果を示し、右側脳室三角部周囲の白質に浮腫を認めた。画像所見から、髄膜腫、脳室内(脈絡叢)転移、solitary fibrous tumor/hemangiopericytomaが疑われた。手術では、栄養血管である前脈絡叢動脈を脳室内で凝固切断した。脳室壁と腫瘤を分けつつ、三角部の脈絡叢は腫瘤とともに摘出した。摘出検体の病理組織所見では、紡錘形の細胞が渦巻き状に増生している所見や、紡錘形の細胞が索状に配列している所見、楕円形の細胞が増生している所見を認めた。以上から、移行性髄膜腫grade Iと診断された。

担当区分：筆頭著者   掲載種別：研究論文（学術雑誌）   国際・国内誌：国内誌  

PURPOSE: The aim of this study was to predict neurological outcomes for acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) using diffusion-weighted imaging (DWI), and assess relationships between anatomical sites of lesions and their outcomes. MATERIALS AND METHODS: We assessed DWI abnormalities and neurological outcomes in 30 patients with AESD, and classified patients into severe and non-severe groups according to their neurological outcomes. We also established a DWI scoring system as follows: zero for normal, and one for lesion at each location. Differences between the severe and non-severe groups were examined, and receiver operating characteristic (ROC) curve analysis was performed. RESULTS: Nine (30%) patients were classified into the severe group. On DWI, patients in the severe group were more likely to have temporal lobe (P = 0.014), perirolandic (P = 0.008), and corpus callosum (P = 0.0008) lesions than those in the non-severe group. The total DWI scores were significantly higher in the severe group than those in the non-severe group (P = 0.0002). ROC curve showed an area under the curve of 0.929, with a cutoff value of five, sensitivity of 88.9%, and specificity of 81.0%. CONCLUSION: Patients with severe AESD had more extensive DWI abnormalities than those with non-severe AESD. Our DWI scoring system may be useful for the prediction of outcomes of AESD. Widespread lesions seemed to have stronger influence on outcomes than each lesion location.

DOI： 10.1007/s11604-020-00984-8

PubMed

拡散強調画像(DWI)の定量解析を行い、痙攣重積型二相性急性脳症(AESD)患者に描出された拡散制限域をスコア化することにより、神経学的転帰を予測し、病変の解剖学的部位と転帰との関連性を評価した。AESD患者30例を対象として、DWI異常と神経学的転帰の評価を行い、神経学的転帰は重症度により、重症群(9例)および非重症群(21例)に分類した。また、正常の場合は0、各部位における病変を1とする DWIスコアリングシステムを適用し、重症群と非重症群を比較した。その結果、重症群では非重症群に比べ、DWI上に側頭葉、ローランド裂周囲および脳梁に病変が多く認められ、DWI総スコアにおいても、重症群では非重症群に比べ有意に高値であった。さらに、ROC解析により、重症群および非重症群のDWI総スコアによる判別を行ったところ、AUCは0.929カットオフ値5における感度と特異度が、各々88.9%と81.0%であることが確認された。以上より、重症AESD患者は非重症AESD患者に比べ、DWI異常が広範囲に及び、DWIスコアリングシステムはAESD転帰予測に有用であることが示され、病変部位ではなく、病変範囲が転帰に強い影響を及ぼすことが示唆された。

20歳代女性。頭痛・嘔気・複視を主訴に受診した、幼少期にTurner症候群と診断されていた。頭部MRIとCT angiographyで左後大脳動脈に嚢状動脈瘤を認めたほか、左椎骨動脈に口径不整を認め、左椎骨動脈解離と診断した。健常な右椎骨動脈を経由してコイル塞栓術を施行したところ、カテーテル操作により右椎骨動脈解離が生じ、原因として脳血管の脆弱性が考えられた。3ヵ月後、右椎骨動脈解離が改善したことを確認のうえ左後大脳動脈のコイル塞栓術を行い、術後3年の現在まで動脈瘤の増大は認めていない。

20歳代女性。頭痛・嘔気・複視を主訴に受診した、幼少期にTurner症候群と診断されていた。頭部MRIとCT angiographyで左後大脳動脈に嚢状動脈瘤を認めたほか、左椎骨動脈に口径不整を認め、左椎骨動脈解離と診断した。健常な右椎骨動脈を経由してコイル塞栓術を施行したところ、カテーテル操作により右椎骨動脈解離が生じ、原因として脳血管の脆弱性が考えられた。3ヵ月後、右椎骨動脈解離が改善したことを確認のうえ左後大脳動脈のコイル塞栓術を行い、術後3年の現在まで動脈瘤の増大は認めていない。

その他URL： https://search.jamas.or.jp/default/link?pub_year=2020&ichushi_jid=J01565&link_issn=&doc_id=20200403110013&doc_link_id=10.18888%2Frp.0000001173&url=https%3A%2F%2Fdoi.org%2F10.18888%2Frp.0000001173&type=%E5%8C%BB%E6%9B%B8.jp_%E3%82%AA%E3%83%BC%E3%83%AB%E3%82%A2%E3%82%AF%E3%82%BB%E3%82%B9&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00024_2.gif