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- Strom, Peter, et al.
(author)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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In: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Journal article (peer-reviewed)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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- Ström, Peter, et al.
(author)
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Pathologist-Level Grading of Prostate Biospies with Artificial intelligence
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Other publication (other academic/artistic)abstract
- Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73).
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- Swärdh, Anna, 1968-
(author)
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Rape and Religion in English Renaissance Literature : A Topical Study of Four Texts by Shakespeare, Drayton, and Middleton
- 2003
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Doctoral thesis (other academic/artistic)abstract
- This study argues that Shakespeare’s Titus Andronicus (1594) and The Rape of Lucrece (1594), Michael Drayton’s Matilda (1594) and Thomas Middleton’s The Ghost of Lucrece (1600) are, in ways hitherto not realised, topically concerned with the religious controversies in the wake of the English Reformation. This concern is discussed on a general level of interest related to religious attitudes and practices significant at the time of writing, and on a specific level pertaining to events surrounding the capture of the Jesuit poet Robert Southwell in 1592, which included the rape or seduction of a Catholic woman. Defining topical meaning from the complementary perspectives of intention and reception, I argue that, while all four texts are topical on the general level, Shakespeare’s and Drayton’s texts signal a topical concern also on the specific level. The study examines thematic, metaphorical and stylistic constituents of the texts’ topicality: oppositional groupings of characters reflecting contemporary “Catholics” and “Protestants”; the theme of rape in a religious context; the depiction of devotional practices such as tearful contrition and image-worship, including idolatrous and iconoclastic positions as well as anti- and pro-Catholic attitudes; references to contemporary persecutions; and influence from Counter-Reformation poetics via Southwell’s writing. While Titus Andronicus reflects the religious strife throughout the Tudor reign with allegorical persistency, I claim, the topicality of Lucrece is especially visible in the complex portrayal of Lucrece’s and Tarquin’s encounter in terms of incorrect devotional behaviour. It is suggested that Shakespeare’s texts criticise the religious politics of the contemporary rule. The study further argues that Drayton’s Matilda shows unreformed sympathies, and that Thomas Middleton’s The Ghost of Lucrece is satirically anti-Catholic.
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